A few months ago I flew to New Orleans for the American Academy of Orthopaedic Surgeons annual conference (AAOS). 30,000+ attendees, hundreds of exhibitors, and my willpower to talk to real decision makers who don’t reply to email or Linkedin because half these people are surgeons or medtech reps who haven’t updated their profile since 2019.

This is the reality for a ton of vertical companies. Your buyers aren’t hanging out on LinkedIn. They aren’t opening cold emails. They’re in operating rooms, on job sites, in clinics, in warehouses. The playbook that works for selling dev tools to Series B startups in SF doesn’t work here.

But you know where these people DO show up? Industry conferences. And if you build an actual system around conferences instead of just buying a booth and crossing your fingers, this channel can be absurdly productive.

However, if you plan effectively and have a strong strategy going into these conferences, most of the time, they’re a complete no brainer. So let’s talk about it.

Why Conferences Hit Different for Vertical SaaS

There’s a reason event organizers say in-person events are their most impactful marketing channel. For vertical companies specifically, conferences solve the two hardest problems in your GTM simultaneously: access and trust.

Access - your buyers are unreachable through normal channels. They don’t live on LinkedIn, they don’t read cold emails, and they might not even have a public-facing email address. But they DO attend their industry’s annual conference because their peers are there, the latest products are there, and their company pays for it.

Trust - in verticals like medtech, construction, manufacturing, etc., buyers are deeply skeptical of outsiders. They’ve been burned by vendors who don’t understand their world. A 15-minute conversation at a booth where you demonstrate real domain knowledge does more than 50 emails ever could.

The math is simple: if your average deal is $50K+ and you can close even 2-3 deals from a $15K conference investment (booth + travel + dinners), that’s a 6-10x ROI. But only if you have a system.

Goes without saying - do not buy the $500,000 booth - it’s not a good use of your $3M seed round ;)

There’s effectively 3-4 touchpoints you can have with folks in your end-market associated with a conference

1. Pre-Conference Outreach

   1. Email

   2. Call

   3. Text

2. IRL Conference chats

   1. At the booth

   2. Coffee chats

   3. Hosting events or going to events

3. Post Conference Outreach

   1. Email

   2. Call

   3. Text

How I do it - Before the Conference

Here’s the actual workflow I run for pre-conference prospecting:

1. Scrape the exhibitor/speaker list - Most conference sites have an exhibitor page. Use Apify or Clay’s Chrome extension to pull every company name. If there’s an attendee list available for purchase, buy it - it’s almost always worth the $500-2K.

2. Enrich the companies - Run the company list through Clay or your enrichment stack. Pull headcount, revenue range, tech stack, whatever your ICP criteria are. Score them the same way you’d score any account.

3. Find the right people - For your top-scored accounts, find the decision makers using Apollo, LinkedIn Sales Nav, or your data provider of choice. You want 2-3 contacts per account ideally.

4. LinkedIn stalking - Search LinkedIn for posts mentioning the conference name. People literally announce they’re going. These are warm targets. Save them.

5. Layer the outreach - Email first (2-3 weeks out), then LinkedIn request with a note (1-2 weeks out), then if you have phone numbers, text or call the week of. The text play is genuinely underrated - I sent ~30 texts for AAOS saying something like “hey, just got into NOLA for AAOS - would love to grab coffee if you’re around” and booked 10 meetings from it.

At the Conference

This is the fun part → in life if you have real chutzpah, you can do some incredible things. For most people its too easy to send an email, and tons of folks aren’t as good at a nice cold-approach. I am no pro, but if you can get over your fear of going up to random strangers at conferences and asking them about their problems, you can pretty much do anything in this life.

Prioritize Your Hit List

However, just approaching anyone is not good enough, you could waste the entire conference going up to accounts that are too small and don’t have budget, or are too big and have built your product in-house. You need to be far more strategic. When i went to AAOS, I took the exhibitor list I captured above, and cross referenced it with my CRM to see the approximate account score for each of the accounts. Then I sorted the list by descending account scores so I could see each company I should go visit, and i could concretely see why → they have 25+ field sales reps, they have 15 managers, they recently were acquired by a PE firm, etc. Going up to an account like this one rather than approaching an account that has a nice booth but no reps in the USA will yield far different results and will allow for your boots on the ground efforts to be fare more worthwhile.

The Approach

Before you approach the booth, try to do a cheeky Linkedin stalk to identify your buyer beforehand if you can. And glance at the firmos / technos so you can use some good references during the chat.

Record Everything (if you can)

Finally, be sure to record the conversation via Granola or otherwise if you can, and get their email / phone number and land some concrete next steps if they’re interested/

At the same time, you’ll be able to have those coffee chats that you scheduled, same logic applies. Do a pre-read on the company, record the convo and summarize it, and get followup contact info if needed.

Finally, go to a few events / happy hours / meals and try to be friendly and meet others.

Build in Real Time (Yes, at the Conference)

One thing I did at AAOS that I think more founders should try: I brought my computer and Figma Make with me to the floor. Not on a monitor at a booth - on my laptop, between meetings.

I’d be talking to a prospect at their booth and they’d say something like “I wish this could show me X instead of Y” or “what if the interface did Z.” I’d thank them, walk to the next booth, and prompt Figma Make to render what they just described as I was walking. By my next coffee chat I had an updated prototype that reflected real buyer feedback from 60 seconds ago. That’s an insane loop. The traditional version of this is: go to a conference, take notes, fly home, debrief with your product team, wait two weeks for a design update, then try to re-engage the prospect who’s already forgotten your name. I compressed that into the time it takes to walk across a convention center.

And look - a lot of people will tell you it’s “too early” to go to a conference. You don’t have product-market fit yet, your prototype is rough, you don’t even have a sales deck. I’d push back hard on that. I went to AAOS specifically because I was early. I wanted to compress months of customer discovery into three days of face-to-face conversations with the people who actually live the problem I’m trying to solve.

You can do 50 Zoom calls over 2-3 months or you can spend 3 days at the industry conference and learn more than you would in a quarter. The prototype doesn’t need to be polished - it needs to be a conversation starter. If anything, showing something rough makes people MORE willing to give you honest feedback because they feel like they can actually shape it. The worst thing you can do is wait until you think you’re “ready” and miss a year’s worth of conferences where your buyers were all in one building waiting to tell you what they need.

How Many Meetings can i get?

If you do all of the above and are able to prioritize accounts and all the tech necessary to have these meetings and make them productive, during a 5 hour conference day (10AM - 3PM), you should be able to have one meeting every 15-20 minutes, or 3/4 per hour. So 12-16 meetings each day if you eat lunch and 15-20 meetings each day if you don’t ;)

Moreover, your goal should be to be an extremely memorable individual - wear a funky shirt or make a lot of jokes - you’ll need your prospects to remember you - if they don’t your post-conference outreach will go nowhere.

At the end of each day, you can have Claude ingest all of your Granola notes, the contact info, and more and 1) update CRM, and 2) send those annoying follow up messages

Post Conference Outreach

Arguably the most annoying part of the process, but where actual conversions happen → if you aren’t nailing the post conference playbook - you might as well have not gone or spent the money to do so.

Drop everyone into some friendly, casual, and low pressure followup campaigns via email, and Linkedin. And have BDRs follow up with anyone via email and phone that doesn’t reply. Be sure to reference tangible points you touched on at the conference.

The Math on Conference ROI

Let’s be real about costs. A typical conference booth for a startup runs $5-15K depending on the show. Add flights, hotels, dinners, and swag and you’re looking at $15-25K all-in for a team of 2-3.

That sounds like a lot until you do the math. If your ACV is $30K+ and you close even 2 deals from a conference (which is very achievable with this playbook), you’re looking at a 2-4x ROI. If your ACV is $100K+ it’s a no-brainer.

The key metric I track: cost per qualified meeting. If I’m spending $20K total and I’m having 40 meaningful conversations across 3 days, that’s $500 per qualified meeting. For most vertical companies where CAC runs $5-10K+, that’s extremely competitive with every other channel.

The conferences where ROI breaks down are the ones where teams don’t do the pre-work, don’t have a hit list, and don’t follow up. They spend $20K to have 5 random conversations and wonder why it didn’t work.

Worst case, it’s a good team bonding event with customer feedback from real buyers that will give you something to think about when you go back home!

Quick note: you don’t need a booth to run this playbook. Some of the best conference ROI I’ve seen comes from teams who just buy a general admission pass, have a killer hit list, and spend 3 days doing coffee meetings and walking the floor. The booth helps (it gives people a reason to come to you), but it’s not required. The system is what matters.

The Conference Tech Stack

• Scraping: Apify, Phantombuster, Clay Chrome Extension

• Enrichment: Clay, Claude Code, Apollo, LeadMagic Wiza (for phone numbers / texts)

• Outreach (pre/post): Instantly (email), HeyReach (LinkedIn)

• At the conference: Granola (call recording/notes), Attio (CRM updates), your phone’s native texting, Figma Make (or prototyper)

• Post-processing: Claude Code to batch-summarize Granola notes and update CRM, Attio Workflows to trigger follow-up sequences

The End

Most teams show up to conferences with no system and leave with a stack of business cards. There’s a full playbook here that few teams actually take advantage of. They buy a booth and cross their fingers. If done right, this can be an incredibly dense and powerful channel

Garrett

Over the last 12 months the tech landscape has changed at a terrifying pace for builders, employees, investors, and more.

• OpenAI and Anthropic seem to release new models once every other week.

• SaaS stocks are down over AI fears as well as over liquidity needs (lol) for last weeks allocation in SpaceX and the upcoming and highly anticipated OpenAI and Anthropic public offerings - all of which require new primary capital allocations.

• Many investors are scared to invest in startups over concerns over OAI and Anthropic simply cannibalizing their entire TAM as part of their product (Figma, and more)

Different symptoms, same underlying shift: the cost of building software just fell off a cliff, and the market is repricing every company whose value was being the software you used to buy.

New models every other week is the supply side of that. SaaS multiples compressing and investors fearing the incumbents get eaten is the market pricing it in. When tools are this cheap to build, owning one stops being a moat - and that’s the thread I want to pull on for the rest of this piece.

Barriers to entry are lower than they’ve ever been. Inspired employees are vibe coding CRMs, ripping out random workflow tools, and more. No commentary on whether these efforts will be successful and yield results, but the momentum to build over buy is there - and I am certainly ambitious enough to try to build my own things as well.

But people don’t seem to know what they should build and what they should buy - and people seem to cite time, cost, and effort tradeoffs as the predominant argument for their path forward.

While these arguments are interesting and cultivate conversations based in numbers, I believe the reality is inherently far more logical.

Before it was coined by Varun Anand and Mishti Sharma at Clay - “Alpha” was actually finance word - it’s the excess return you earn above the market.

For a startup, “the market” is every competitor with access to the same playbook, the same tech stack, and the same hiring pool - so alpha is whatever you do that they structurally can’t copy.

And this is an element that is difficult to replicate when done right; the lead magnets you see today, were “alpha” 6+ months ago, that now are being used to engagement-farm and send emails to prospects who are drastically behind and looking for a quick fix.

Unless you’re in a market that hasn’t used this strategy before, you’re not commenting “PLAYBOOK” for something that will make your company go from unheard of to an overnight success.

To zoom out, I think we can broadly analyze what all of this means.

Capitalism is simply about building the best long-term profitable business with the most sustainable margins and the deepest competitive moat.

And building a company is (at its foundation) about two things at its core

1. Building faster and more efficiently than anyone else

2. Selling more and more efficiently than everyone else

It’s every man (or woman) for themselves, and building / selling are at the very epicenter of that world.

If you take a step back and look at these two parts of running a business - it is your duty as a founder, employe, shareholder, and so on and so forth → to build and sell as fast and effectively as you can.

And for most companies, buying SaaS that is democratized for the masses; well put simply, that simply will not allow you to build the best company in your space. It will make you just like everyone else.

That’s why you see companies like Ramp, Modal, and others staffing internal teams focused on driving efficiencies internally rather than procuring products. How can we work with our sales team, with our engineering team, to turn the wheel faster - to ship the next feature, to sell one more deal this month.

From all of this, it seems to me like there are two types of software that exist today and we will continue to see the divide between the two get larger as time goes on:

1. Ops Software

2. Alpha Software

1. Ops Software

In my mind, ops software is software that is part of your operational stack - but does not allow any business to win their market.

Things like your CRM, your accounting tool, your productivity suite, payroll, etc.

It’s likely your team can augment these tools with vibe coded solutions, random annexes to existing solutions, and more.

I’m coming up with this as I go along, but I think historically, lots of tools that were at one time Alpha Software, land up becoming Ops Software.

When CRMs were first created, they were probably an enormous game changer for businesses, allowing them to document their deals, contacts, and companies. But as the market matured and this database became table-stakes, it becomes a stable, core part of every companies workflow.

What AI actually democratizes is the ability to manufacture your own alpha - the tooling, not the edge itself. The edge still has to be produced, by you, over and over.

If ops software is the floor every company stands on, alpha software is the ceiling only you can reach:

Alpha Software

Alpha software is my way of articulating the software that will truly make a competitive difference in your company.

Alpha software does not look like typical SaaS; by its very nature it needs to be custom built, hyper bespoke, catered to a teams exact needs and has the ability to evolve on a dime to match evolving market conditions and the will of its operators.

• Your team wants a faster dialing experience? Pushed to prod

• Your team needs custom integrations to 3 new purchased data vendors? Done

The way I think about it, team’s should outsource (buy) their ops software, and create (build) their alpha [software]. Buying the thing everyone has is rational - reinventing payroll is a waste. Buying the thing that’s supposed to win your market guarantees you’ll perform exactly like everyone else who bought it.

Time, cost, and effort are not the metrics to anchor to, its whether the tool is a source of your edge, or simply the cost of doing business.

To play devils advocate, if everyone can vibe-code a bespoke dialer on the same day, hasn’t the ability to build alpha itself been commoditized? If your competitor ships the same tool by dinner, where’s the edge?

I guess the logical resolution here comes back to the new perspective on the way competitive moats are built and loyalty prevails - thru taste. When owning the tool is free, the durable advantage becomes taste and speed - knowing which tool to build, building it before anyone else, and killing it the moment it stops working. The bottleneck is judgment, not engineering.

And on top of this, we’ve all seen it happen, vibe-coded tools are where tech-debt is created - these things decay, someone has to maintain them, and when the original champion of it departs, so does the lineage of the tribal knowledge of what made it special in the first place. Historically that maintenance cost is why buying usually won.

Where this has changed is that the same drop in build cost applies to upkeep - rewriting, patching, and extending a tool is now nearly as cheap as standing it up. The math that used to favor buying has quietly flipped.

Buying Gong or Clay will never get you fired, but it probably won’t make a category winner and put you on a yacht (sorry).

You can watch this ops/alpha lifecycle play out in real time in the two functions every company lives or dies by - selling and building

Selling

I say this because the reality is most GTM companies today are one historical example of an exceptional growth tactic that a handful of companies leveraged, that someone decided to commoditize for everyones benefit.

While this creates a initial golden age of innovation for those first few adopters, the second this tech is democratized and becomes available to everyone, the returns become diminishing - it no longer will give you an edge

We’ve seen this with nearly every flavor of outbound -

• emails (Apollo, Outreach, Salesloft, Unify),

• phone calls (Nooks, Orum, Trellis),

• gifting (Sendoso, Giftly),

• and much more (that one cake sending company that some dude vibecoded?).

Building

The same can be said for coding / development tools.

Cursor, Cognition, Copilot, Claude Code, Windsurf, Zed - they’re all incredible coding platforms that we’ve seen over the last 24 months.

Tools no longer have loyal users; users are loyal to whichever platform gives them the ability to generate differentiable outcomes the longest.

And whichever is the best tool du jour will win that battle.

This is a foundationally (not a word but alas) new reality.

Takeaways

I don’t think is a relatively novel thought, this is the nature of competition. And so long as capitalism is about building and selling, teams will put their money where their mouth is and buy whatever promises to give them a 2% edge over the competition.

With the cost of building dropping to zero, and the time it takes dropping in parallel, the future is bespoke. And that future is an exciting time to live in.

So, go out and ship, and build what will give you the competitive edge to beat your competitors and capture your market. There’s less than there ever has been standing in your way.

Garrett

This is the most common failure mode in TAM scoring. Well, it’s the most common problem that i’ve run into, and I haven’t spoken to many people about the problem. The model looks right. The weights feel right. But the output is useless because the distribution is flat.

What do I score on?

When I first start building the model, I try to identify a few things

1. What are the firmographic datapoints → These are criteria like employees, industry (we’ll get to this complex one) revenue, country of the HQ, among others

2. What are any key technographic datapoints → These are things like tech stack (which is overpriced AF in credits), whether someone has a javascript snippet on their website (s/o RB2B or Unify), or even what items they mention using in their job postings

3. What are some researchable datapoints that we can investigate, classify, or surface using AI, data vendors, or some combo of them → For me this typically looks like trying to identify how many of a certain profile of person work at a company, such as: sales reps, revenue cycle management professionals, IP professionals. It may be classifying the company as one of multiple potential industries rather than using a generic industry classification, or even distilling various levels of involvement in certain types of initiatives based on website contents.

A criterion I don’t score on - is intent - notably because we want to know which companies are a good fit in general, and then the ones that are in market today, we will devote special attention and detail to. We don’t want our scoring model today to permanently favor companies that had a new hire last month.

This tees up the actual scorecard, and then we set the values for each one.

What’s next? The part folks skip

This is how most exercises go → build the scorecard, assign some roughly justifiable values, and then sync the results to CRM. In that, most scoring conversations focus on what to score: firmographics, technographics, fit criteria. That’s table stakes. The part that actually determines whether your GTM team can act on the model is the shape of the output.

What I’ve landed up doing most of the time, is building a scoring model that clusters 60% of accounts within a 5-point range. This helps no one, and certainly not the GTM operator running the system.

It’s a shame, you can build a scorecard in a way that feels right but because you don’t know the actual shape or output of every datapoint from a 35K foot view, you won’t build a nice smooth distribution.

Well that distribution ain’t so pretty

The setup: ~45K companies pulled for a vertical SaaS product. We’d built what looked like a serious scoring model: 9 weighted criteria - industry, rep count, PE backing, sales motion, tech stack, catalog complexity, growth, revenue-per-rep, fragmentation - summing to a 100-point max, with fit and complexity multipliers on top. It looked rigorous. It was useless.

What actually happened:

• Half the list - 49% - disqualified before scoring even started.

• Of the survivors: median 0.25, mean 0.29.

• A wall at the bottom: ~29% of accounts piled into the lowest decile (0–0.10). The single biggest bin by a mile.

• Not even a clean skew - a spike at the floor, a second lump around 0.20–0.40, then a thin decaying tail. Dead zones in between.

• The top of the scale was empty. Raw points topped out at 84/100, and the 99th percentile was 72. The handful of accounts showing “perfect” scores were multiplier artifacts, not real best-fits.

What happened?

1. Dead axes inflating the denominator.

   1. Two criteria barely fired. Growth scored zero for 100% of accounts - it never worked at all. PE scored zero for 93% (average: 0.33 out of 15 possible). That’s 20 of 100 points that essentially never paid out. Add tech (53% zero) and rep count (60% zero), and most of the weight budget was unreachable for most accounts. The denominator said 100; the realistic ceiling was ~72. Every account’s percentage got divided by a number it could never hit, so the whole distribution compressed toward the floor.

2. Missing data scored as zero.

   1. The model couldn’t distinguish “bad fit” from “no data.” An account we hadn’t enriched looked identical to an account that was genuinely wrong. That’s what builds the floor pile - absent data punished exactly like a mismatch.

3. Mixed populations in one model.

   1. Half the list was off-vertical, DTC, or wrong-segment - companies that by definition score zero on the criteria that matter. They weren’t low-fit accounts. They weren’t in the TAM at all. And they dragged every summary statistic down with them.

4. Multipliers manufacturing noise.

   1. Multiplying the additive score by fit and complexity factors buckets accounts onto a handful of discrete products. That’s what created the jagged, multimodal shape - and the fake cluster of “perfect” scores at the top.

The fix is kinda boring but it works

The fix isn’t a fancier model. It’s a few mundane things:

1. Weight by variance, not importance.

   1. The instinct is to weight axes by how important they feel. “Industry fit is critical - give it 22%.” But if most of your survivors are already in-industry, that 22% adds a constant, not separation.

   2. Weight proportional to what an axis actually spreads: roughly coverage × distinct values. We dropped country from a real weight to 2 points (everyone scored the same on it) and moved that budget to revenue, employee count, and catalog depth - high-coverage axes with many bands.

   3. The one exception: a high-value, low-coverage signal. PE backing fires for under 8% of accounts, so pure variance-weighting would delete it - but it’s the whole thesis. So it stays in, as a tie-breaker, not a spread driver. Don’t let the rule kill your best signal.

2. Disqualify aggressively, before scoring.

   1. Accounts that score zero on every axis aren’t low-fit - they’re not in your TAM yet. They’re noise that compresses everything else. We set aside 58% of the raw list before scoring.

   2. Harsh, but it left a population where every survivor had something to differentiate on. (Set aside, not deleted - a company that gets PE-acquired or crosses the headcount floor next quarter comes right back in.)

3. Go pure-additive.

   1. Kill the multipliers. Multiplicative scoring feels precise and produces garbage - discrete buckets, dead zones, fake perfect scores. Straight weighted addition spreads cleaner every time.

4. Normalize into a bell curve.

   1. Raw scores bunch in the middle no matter what. Rank every account, run it through an inverse-normal CDF, and the output spreads across 0–1 with a natural bell shape. Same ranking, better separation - a rep can finally see the difference between a 0.72 and a 0.58. And yes - rank-normalizing imposes the bell shape rather than discovering it, but that’s the point: the normal curve isn’t a claim about the “true” distribution of fit, it’s a coordinate system built for action. The ranking is the truth; the bell is just the most legible way to space it out so a rep can tell a 0.72 from a 0.58.

5. The model is never checked against your actual customers.

   1. You tuned weights until the output looked right and the top 15 were “names you’d recognize.” That’s memorization, not validation.

   2. The real question: do your closed-won deals and best current customers land in the top tier? If your Performance Food Group / Revance-type accounts score at the median, the rubric is wrong no matter how pretty the histogram. Distribution hygiene and ranking accuracy are two different failures, and the draft only treats the first.

6. Correlated axes silently re-weight the model toward one hidden factor - usually “size.”

   1. You think you have 9 independent signals. You actually have ~4. Headcount, revenue, PE backing, rep count, and growth all co-move with bigness.

   2. A large company scores high on five axes that are all proxies for the same thing, so “9 weighted criteria” quietly becomes “size, counted five times.” This is invisible on a histogram - the distribution can look great while the ranking is dominated by a factor you never meant to over-weight.

7. Use Claude Code / Codex to AB test better distributions

   1. To get here the first few times I literally fed the Clay table to Claude along with context and point values / criterias and it will share how to improve

The order matters: fix the weights, disqualify, go additive - then normalize. The bell curve is the last step, not the first. Run it on a broken rubric and all you get is a good-looking wrong answer.

The deeper lesson: two of those fixes aren’t about scoring at all. They’re about not letting missing data and out-of-scope account dominate your statistics. Score quality was never bottlenecked by rubric cleverness. It was bottlenecked by data coverage and population hygiene.

How things improved

After we made these changes, life was looking quite a bit better :)

• Median: 0.25 → 0.50. The mass moved off the floor and into the middle, where you can actually work it.

• The floor pile dissolved. ~6,500 accounts bottomed out before; under 3% sit in the lowest decile now.

• A clean bell, full range. 0.00 to 1.00, smooth, no dead zones - instead of a spike and a tail.

• A real top tier: 2,001 accounts above 0.75, and 7,482 above 0.55 - a usable best-fit cluster, not a scatter of artifacts.

• The top 15 are all names you’d recognize

Why this matters for TAM Score distribution

A compressed score distribution breaks everything downstream:

• Tier assignment is arbitrary. If 80% of accounts are within 5 points of each other, your A/B/C tiers are random. Reps don’t trust them.

• Routing is random. Score-based lead routing becomes a coin flip when scores are clustered.

• Prioritization doesn’t work. “Work your top 50” means nothing when #1 and #500 are 3 points apart.

• Outbound sequencing has no gradient. You can’t write different copy for different tiers if the tiers aren’t real.

A good distribution is the precondition for everything your GTM team wants to do with the model.

For the future

Before you ship your next scoring model, check these:

1. What’s the stdev of the output? (Target: ≥0.12 on a 0-1 scale)

2. What % of weight goes to saturated axes? (Target: <15%)

3. What % of your TAM scores 0 on >half the criteria? (Those aren’t low-fit - they’re not in your TAM)

4. Do your buckets have gaps? Plot the histogram.

5. Is the output bell-shaped or cliff-shaped?

If the distribution is wrong, the model is wrong - no matter how good the criteria look on paper.

Garrett

On April 1st I was asked to give a talk at Deepline’s April Tools Day. I spoke about bespoke sales tooling, the value in building truly custom workflows, and the much greater potential ROI vs commoditized GTM SaaS products.

Feel free to check out the speech here:

Small detail: I had spent less than two hours in Claude Code when I gave that talk.

I’d been on Claude Cowork since February, and I’ve been on Cursor since last spring, so I wasn’t a stranger to AI tooling - but the specific “Claude Code for Clay” thesis I was pre-pitching? I could smell it and certainly see it (as I said in the speech), but I hadn’t been cooking it up myself just yet.

Every part of the talk I gave was real, and the webapps I have built for folks and things I have done are real, but replacing Clay with Claude Code - was still so foreign to me.

After I meme-d my way thru the talk I decided to find out if I could do it. In early April I stopped taking on new clients for two weeks, blocked the calendar, and went deep on figuring out wtf Claude Code was actually capable of for GTM Engineering.

To say I saw the future would be an understatement…

A note on Clay before we go further

I’m an enormous Clay fan and supporter (FWIW!!!!). Most of my mental model for what a modern GTM workflow even looks like was shaped by Clay. So when I talk about Claude Code below, Clay is the natural reference point - not the antagonist. I think the pricing change just happened to land at a moment where a lot of operators (myself included) were already curious what was possible on the other side, and it created a permission structure to actually find out.

This isn’t a “replace Clay” piece. It’s a “here’s the unvarnished version of what 50 hours in Claude Code looks like, and where my brain keeps reaching for Clay as comparison” piece.

What was built

I’m gonna use this as a quick note to share what I’ve built and how powerful it is, and you can read into pros and cons below if that’s what you’re here for

1. Uses Deepline and Apollo / Clay to webhook leads that match title criteria into our database

2. Cron job runs that filters out bad fit folks, enriches them with LeadMagic, Apify, and a few more providers to pull in all the relevant person level details

3. Deepline / Claude qualifies and scores folks on a 0-100 scale by persona, and enriches anyone with contact deets if their score hits a threshold

4. High threshold candidates go to Instantly, top threshold candidates go to Linkedin

5. Replies are piped into Slack and replied to using a custom AI bot directly from Slack

6. We run recurring drip campaigns for various events, as well as targeted outbound campaigns for various client searches.

This has been a ton of fun to work on. And big s/o to the Coastal Team (David, Asher, and Kyle) for giving me the reins to build something awesome here, and to be future facing enough to take the risk on building something truly unique

As usual, lots of smoke and mirrors

Claygencies and lead-gen experts posting big diagrams, posting leadmagnets, and claiming they’ve built an outbound engine that has solved world hunger.

The reality is, this tech is still very young (partic for GTME), and few folks are leveraging end to end workflows, let alone being used in production at scale.

Most of the work is being one in one-offs or for test workflows.

Gamechangers in Claude Code

Now lets jump into where I see the differentiators for Claude Code

• Speed.

  

  • Gone are the days of building 4 Clay tables with 80+ columns, stacking merges, custom-building waterfalls. Tell Claude what you want and watch the workflow get constructed in real time. A scoring pipeline that used to take me a week or two to bullet-proof now takes an hour or two.

• Code-first, not AI-first.

  • A lot of what I’d been spending AI tokens on is deterministic code in disguise: does this title contain “staff”? Does this school contain “Berkeley” or “Cal”? Does this bio contain “beat quota”? These are string matches. In Claude Code they become Python files that run for free forever once written. Across hundreds of thousands of rows, the per-row cost difference is genuinely meaningful - you stop paying tokens for things that aren’t actually language tasks.

• A/B testing and rescoring.

  • Calibrating a scoring model used to mean heuristics, manual tracking, and pushing more volume toward what works week over week. In Claude Code I just ask. “Reweight `senior_eng` to value tenure higher and show me the new top decile.” Done in a sitt. Test, score, push, repeat.

• Complex Logic

  • Clay works up until a limit - cell size limits, complex logic for choosing who to email, when the last time you emailed them was, etc. → this is hard to enable with Clay. Claude Code can use Supabase to index characteristics and run simple DB operations. This is beyond the scope of 95% of Clay’s typical users needs, but is key to scaling your outbound motion.

• “Use every part of the animal”

  

  • We get so much data from Clay, data vendors, etc. - but you are fundamentally handicapped by the 100 column limits. I end up only using 10-15 datapoints maybe. With Claude code I can use every data point from where someone went to school (all 2+ educations), as well as their follower count, how many promotions they have had, their bio, headline, and so much more! I score most of these which gives me far smoother distributions in ways that aren’t possible with Clay.

    ---

How I think about it next to Clay

The way it lives in my head: Clay is reliable and auditable, Claude is a teammate built for speed and fun.

A spreadsheet is auditable by default. Every column is a row of logic you can stare at. Every change is visible. The cost is the ceiling - there’s only so much you can stack into 80 columns before the marginal click stops being worth it.

A teammate is fast and inventive by default. They can build you a whole pipeline in a sitting, including things you didn’t know you needed. The cost is opacity - it lives partly in code and partly in conversation, and you have to actively interrogate it to know what it’s actually doing.

The product that puts a spreadsheet-shaped audit layer on top of an agentic-teammate-shaped tool is going to be a generational GTME platform. Whoever ships that wins.

Where I screwed myself: silent failures

These aren’t really Claude-specific - they’d happen to anyone writing pipelines by hand. But a spreadsheet UI hides them behind a column view and a CLI doesn’t. You feel every single one.

1. Silent failures are the default.

Nothing in our stack threw an error when a parser wrote 91% of senior-engineer work history into the wrong database column. The scorer read the right column, saw nothing, and silently dropped thousands of qualified candidates from outreach. Same energy: forgot to assign senders to your Instantly campaign? It’s not going to send. 200 OK on the push, no email leaves the inbox ;)

2. Vendor docs lag vendor behavior.

A contact-enrichment API returns `200 OK` with an empty payload for private LinkedIn profiles - indistinguishable from a successful pull with nothing to return. We kept re-paying for the same dead URLs until we built our own “empty-but-successful” tag.

3. Schema drift is constant and invisible.

Six different scripts had each forked their own copy of the same NYC/SF location keyword list. One of them slowly drifted to include Bellevue and Seattle, leaking candidates from the wrong cities into a client’s San Francisco-only campaign.

4. Regex-based classification fails quietly with a score of zero.

Our “Staff Engineer” accept list matched the literal string “Staff Engineer” but not the much more common “Staff Software Engineer.” 6,996 staff-level candidates - 80% of the pool - were silently scored zero with no error to surface the mismatch.

5. “Success” at the API layer can hide failure at every downstream layer.

For four straight days every scheduled job on my Mac looked queued and healthy. macOS was silently rejecting all of them because background processes need an explicit permission grant to read iCloud-synced files. Nothing in the logs said so.

6. Pushes succeed at the API and still land nowhere.

A campaign accepted 40 leads and returned `200 OK`, but the campaign had no senders attached. All 40 quietly never sent. We only noticed when a recruiter asked why a prospect had gone cold.

7. Small inefficiencies compound into thousands per quarter.

We were paying a managed proxy 2x the cost of calling the same enrichment API with the key we already owned. Small per-call delta. At hundreds of thousands of calls it added up to a $1,400+ leak before anyone audited the per-row spend.

Where Claude Code itself is still genuinely hard

These aren’t pipeline hygiene problems. These are agentic-execution gaps that no amount of “write more auditors” will fix. These are the open product questions I’m sitting with.

**Rate limits.** Vendors have wildly non-uniform per-second / per-minute / per-day caps. Claude sometimes recognizes a vendor’s documented limit and respects it. Sometimes it doesn’t, and pretends the limit doesn’t exist until a 429 comes back and the run is half-done.

**Repeatability.** I want Apollo people search → LeadMagic → Apify → JS scoring → Instantly to run the same way every time. Sometimes Claude does the whole flow. Sometimes it skips a step. The state of the workflow lives partly in code and partly in conversation context, and only the code part is durable.

**Updates and deprecations.** Apollo recently deprecated a people-search endpoint and I had no idea - found out when something silently 404’d. Endpoints, pricing, rate limits, inputs, outputs all drift. How do I get Claude to surface and migrate to new endpoints on its own as the world moves? Open question.

**Waterfall design.** Deepline abstracts away the multi-API-key problem, which is great. But knowing which vendors exist, which to try in which order, and which to fall back to - is that part of the GTME alpha? Or is the alpha purely ideation + execution, with the vendor stack as a commodity underneath? I genuinely don’t know yet.

**Cursors + pagination.** Some vendors burned hundreds of dollars in credits because we couldn’t reliably paginate or resume a cursor mid-run. Agentic tooling that doesn’t handle pagination state cleanly is a money leak waiting to happen.

Whoever wins will build the auditability layer

Every single silent failure on that list was discovered by writing an explicit auditor *after* the fact. The hard part of building data pipelines - agentic or otherwise - is that the system can’t tell you what it doesn’t know to look for. You have to interrogate it.

The talk I gave on April 1st was right about the destination and wrong about how close we are. The tooling that wins won’t be the one that does more - it’ll be the one that lets a second operator pick up where I left off without re-discovering every silent failure I already wrote an auditor for.

If you’re a GTME and you’ve been curious: pick one workflow you already know cold - a TAM build, a persona scoring pass, an anniversary trigger - and try porting it into Claude Code over a weekend. Don’t try to rebuild your stack. Just one workflow. The point isn’t to migrate. The point is to feel the shape of the tool against something you already understand.

Let me know what you think and what you’re building! And check out OneGTM for my help if you need it!

This learning has only accelerated during the last 6+ months while i’ve been running my own GTME agency. I’ve quickly speedrun thru GTME builds for teams like Stuut, SuperDial, Antimetal, Patlytics, and many more.

The one thing I continue to come back to over and over again is “how to build culture”. To me, I think it’s the most important thing a founder can focus on, or really anyone who wants to be a core contributor to their Company.

How can I create an environment that people want to work in.

I don’t even know how to define Culture, but think it’d be fair to say:

Work life balance no longer exists (i think)

When I was in banking working late nights and weekends, my parents shared with me that when they were kids, they couldn’t be reached as frequently via phone or computer. Their work lives slowed down (or stopped) when they left the office as they simply didnt have the tech for their bosses to chase them down. Their work place was just for work.

And if their bosses did need to reach them late at night or on a weekend

“it was a bigger deal, so they would have to call dad to get me on the phone for example, so it was more obvious that they were breaking boundaries to a degree and most senior people felt bad about it and appreciated the dedication. All of that seems to be taken for granted now.” - my mom :)

This created a natural barrier between work and life.

Now with everyone a slack message or phone call away, that divide no longer exists.

In banking and PE and even in startup land I’ve been pinged at all hours needing to provide results, comms, or assurance to teams. It’s part of building something great - and i’m here for it.

In a world where work can reach you anywhere, anytime - I fundamentally believe that if we want to ask people to work more, work needs to be a place that is about more than just work. It needs to be a place that is personally fulfilling (of course). But moreover it also needs to be a place that is social and fun too.

And frankly, I think that is at odds with many work environments.

The expectation teams have is for people to work more, but nothing else gives.

Maybe it’s about creating barriers and setting expectations, but it feels hard (for me at the very least).

Be Friends with Your Coworkers / Work with People You Like

If we crunched the data, I think (and I hope) that some of the best teams of the last 5-10+ years are ones that have managed to mix the importance of work, fun and social, and more.

When I think of Ramp, Pylon, Cursor, and more - I think of teams who have managed to do it all. A collection of individuals who want to win, but actually care about one another to the point where they do want to hang out outside of work. Folks I know at Ramp go out to dinners 3-4x a week, outside of the office. It’s about work sure, but it’s also about more than work too.

When I’m friends with my coworkers, opening up my laptop with them on the weekend is never an issue, nor is it when we’re laughing working thru the wee hours of the night.

Sure you work on weekends and late at night because you want to win, but you also do it because it’s fun. And sure it can be fun because you’re building something big and you have an immense amount of responsibility, but also don’t you think it’s because you’re working with friends? After all, your coworkers have become your friends... If you’re devoting 12+ hours of your day to the people sitting next to you and they’re not your friends (or some flavor of it)…. what’s the point?

But my question is, how can we create this space? How can we continue to cultivate it? And dangerously, is it possible to build a big company with strong culture?

Is Culture Only for Startups?

I think that most teams probably start out with strong culture. To me, the best early teams feel more like family anyways. It’s a group of people who know they’re trying to do the impossible. Pylon has said from a very early stage, they’re trying to kill Zendesk… Along with this there’s inside jokes, regular banter, recurring themes and behavior, and traditions.

It’s a small, talent dense group of people who know what they’re getting themselves into and know why they’re doing it. They want to learn, they want to build, they want to be part of a 0-1 story, and they want to have concrete impact.

That’s what I want at least - to come in to work every day, know everyones names, and get to interact with each person in some capacity. To be able to get to know everyone and sit at lunch with them or hang with them on the weekend here and there..

As a funny sidebar, one of my best work friends from Morgan Stanley, Sydney, still gets angry at me for introducing her to others as my “work friend” → we have spent so many hours crying and laughing and having fun together that she insists she deserves the dropping of the word “work”, that we’re just regular friends!

But maybe that state of building is reserved only for early stage teams…. (still one of my favorite substackers out there) wrote this piece back in July of 2025 that has stuck with me to this day:

To the extent that there is anything romantic about startups, most of it comes before all of this [normal startup work]. It isn’t entirely right to say that startups are innovative and creative in their early days, and calcify when they start trying to “sell to the enterprise,” but it’s not entirely wrong either. Early in their lives, startups kind of are that idealized passion project; once they begin chasing quarterly targets, they become a business, and all the jobs that come with that.

It is also in that transition when a company’s ambition—the real, de facto, on-the-ground ambition, not the phantasmic mission statement that everyone has to pretend to be motivated by - gets defined. Prior to building all of this operational infrastructure, startups really can build what they want, hire who they want, and be what they want. They can pitch big visions or narrow ones; small improvements or revolutions. They are kids, still taking prerequisite classes and trying to decide what to be when they grow up.

I think this is interesting → at a certain point an idea will become a business, and the business needs to reflect that. By the time you raise a Series B, you need a financial model (if not sooner), you need BizOps and procurement people, maybe you even need HR or Legal folks.

But maybe I want to stay a kid forever (in the culture sense), and build a company around that.

But maybe AI is changing how we hire

But maybe AI and the infrastructure of today afford a new benefit to the startups of the AI age, to build a Company that can scale further than any before, and do it in a way that allows them to keep that starry eyed feel.

A few weeks ago, Monte Carlo laid off 30% of their staff. Not because they were underperforming, but because management realized we have entered a new paradigm of building with AI. In Lior Gavish’s public post about it, he shared

We’re operating in an era where AI is compressing what used to take large, specialized teams into something that a smaller, more focused group can now do — faster, and better. We’ve seen this firsthand inside Monte Carlo. PMs shipping working features. Support teams closing issues they would have escalated a year ago. Sales Engineers shipping new integrations for our customers. The productivity unlock is real, and it’s accelerating.

Does this mean we can make organizations flatter, use tools for increasing amounts of leverage, and we can empower teams to do more with less?

Culture is Founder Led

And so with my second point, I think culture is truly founder-led / top down. When someone is your boss, you work for them, learn from them, you complain about them (to your s/o, your friends, your coworkers, maybe even your therapist). You are, for better or worse, learning from them.

• If your founder prioritizes meeting new hires and getting acquainted with them, you’re likely to do the same

• If your founder chooses to talk about work at a team dinner instead of your hobbies outside of work, you’re likely to do the same

• If your founder is willing to make jokes or have a drink with the team, you’ll most likely be willing to do the same

• If your founder shows empathy when you lose just as much as they celebrate you when you win, you’ll remember it and choose to act the same to coworkers

I think this must be such an underrated part of being a founder - not only acting as a professional role model for your team (working hard, demonstrating output, etc.) but also a role model in other aspects too. A lot of founders are first timers, so can’t be too critical here (and I certainly can’t relate (yet)), but nonetheless

I was invited to join the Antimetal team on their offsite a few weeks ago. They have ~20 people on the team, and even as an outside consultant, I was touched by the closeness that I felt the team shared. It warmed my heart and made me remember what makes doing this with people you care about so special.

There were two moments that stood out most to me:

• On the second night, the whole team got together in our living room and everyone played Fishbowl and Headsup together - there was laughing and a shared sense of belonging that frankly I missed not being a FT employee; but it also reminded me of how important these moments are to have outside of the workplace with your coworkers. These moments create a shared bond between coworkers that transcends simple work riffs or other laughs.

• At a BBQ dinner, Matt and Shreyas took the time to walk around to each group of people to eat and talk with them. Shoot the shit and make sure everyone knows that they care about them. We can all choose who we spend our time with outside of work, and I am careful to spend time with the ones that I want to prioritize because I want them to know I value them. Matt and Shreyas chose to do this with each and every team member. It’s simple, but it’s so f*cking important.

Finally, a former Unify colleague put it well recently - leadership posture maps back to Machiavelli: as a leader, you can choose to be feared or loved. Either works. What doesn’t work is trying to be both without ever making clear which behaviors earn which response. Your team is always reading you - you might as well make it clear what earns either response.

Want to be loved? Act like it. Want to be feared? Act like it. Want both? At least be explicit about which behaviors earn which.

So what does this all mean

I’m not entirely sure. To build a big company I still think you need a ton of people.

But the companies of tomorrow may be even more efficient than the ones today. With nearly every vertical of organizations being collapsed by verticalized AI tooling (for that org) it feels like we could exist in a world of 2-3-person organizational pods.

Sales, Marketing, HR, People, Customer Success - if we’re building AI to automate all of this, shouldn’t we be able to plug 2-3 of the very best into each of these roles and enable them to build a Company with the same scale as the companies of yesteryear?

In a big company, teams instead can become very close. Skyler Mickunas has done a phenomenal job at this with the BDR squad at Unify. He hires the best bizdev reps out there, and if asked all of them will go to bat for him. They hang on the weekends, and the sense of camraderie is palpable.

Compensation trends are changing as well - if you’re hiring less and your employees can do more, there’s rationale to explore improving peoples equity compensation and cash comp.

No VC wants to hear that you want to have a small team, and If I want to go solve an enormous problem I certainly don’t think its easier to do that with a smaller one; but I do want to find a way with the technology of today to build scalable systems that enable us to build something for our company that is fun, fulfilling, and important.

So what could this look like? (cue spongebob meme)

• Founder led - Founders who are incredible executioners and also focus on building a team. Employees want to work for people they respect, people they look up to, and also good, down to earth humans. In a leaner world where we can move faster - it is ever more important to breed and cultivate a culture that you want for the lifeblood of the organization.

• Show empathy and be human - this is probably my biggest belief - as a leader and as an employee it is so important to be human and show empathy to everyone especially when things aren’t going your or their way. These moments solidify culture and show people your true character. Treat others the way you want to be treated and act gently to others :)

• Give a shit - Teams who actually give a shit - not just about work, but about you, and about getting you to stay.

• Be fun - Crazy right? Create an environment with laughs, (appropriate) jokes, and one that makes people smile. Host random hackathons, sales spiffs, games, and touchpoints that people will remember and look back fondly upon. Give people something to smile and tell their families about.

• Intentional team building - When you’re invited to a company sponsored happy hour, you know its about selling other companies on your product. If I have to choose that or doing something I enjoy, I’ll probably choose the latter. If teams can create events that that doesn't consist of just talking about work, I think people will enjoy it more, and will choose to attend that more often than not in the future.

• Emphasize atypical cross-collaboration - Create an environment where everyone collaborates with eachother and everyone spends time with one another. Make sales actually hang with engineering. Make design interact with finance. Don’t allow people to be strangers..

• Avoid company politics - If you hire people to handle a function and they deserve or show the ability to lead it or contribute more, empower them to do so. If you hire folks who expect an ability to grow into a leadership role, pave the path for them to get there. Avoid people who play games and report on things that don’t matter or massage leadership to think they’re solid.

• IRL (mostly) - body language isn’t read as well over Zoom. Figure out a way to plant your HQ where the vast majority of your team is. You’ll work faster, harder, and get to riff with people IRL and not just send passive aggressive messages via Slack.

• Hiring slowly and deliberately - culture breaks fastest when you hire for headcount instead of fit. Every bad hire taxes the whole team socially, not just functionally.

• Shared suffering - some of the tightest teams are forged through hard moments together, not just happy hours. The Antimetal offsite works because it’s earned, not manufactured.

• Rituals that repeat - the Fishbowl story is great because it’s the kind of thing that becomes “our thing.” One-off events don’t build culture, recurring ones do.

• Transparency from the top - founders who share the real numbers, real fears, real wins create psychological safety faster than any team building exercise.

What do you think? What matters, what’s unavoidable? How can I do this bet someday?

Last week I dropped the first ever state of GTME report.

GTM Engineering is one of the fastest-growing roles in tech, yet almost no one understands how it should be structured, compensated, or evaluated.

After repeatedly negotiating compensation for myself and peers without reliable benchmarks, I realized the data simply did not exist.

So I said f*ck it, i’ll build the compensation benchmarking report myself.

If you havent seen it yet - go view it on the web at stateofgtme.com

I collected 225+ responses from GTMEs across startup stages from Seed to Public, spanning North America, Europe, and APAC. Data was anonymized and self-reported. No sponsor had any influence on survey questions or answers. :)

“GTMEs are going to make a bazillion dollars”

It’s very easy to virtue signal and say you’ll pay large sums of money to GTMEs but to actually do it is another thing...

Right now its the wild west for GTMEs. Some USA based GTMEs are making 65K all in; others are making north of 250K…. It’s fair to assume there are age, experience, and technical differences between these people, but those people may not know the other exists!

The same is true of agencies. Some agencies are charging $2,000/mo, while others are charing $33,000/mo or more (many high end agencies declined to provide compensation data…)

So I literally just went out and brute forced this thing, sent a bunch of emails, texts, posted on Linkedin, and partnered w reputable people like and Maja Vohje for distribution and more. Thank god I have friends because we were able to get 225+ responses :), many of whom I know and trust! This report is a testament to them just as much as it is to me - theres tons of anonymized, sensitive data about comp, trends, and more.

Generally speaking, if people generate results in the form of pipeline generated, time or efficiency created, or overall work satisfaction, I believe those people should be paid within certain market rate paybands. This report will hopefully help GTMEs everywhere advocate for themselves, teams learn what they should expect to pay in-house and agency GTMEs, and more. Time to put your money where your mouth is ;) - you know who you are.

Takeaways

There’s a dozen takeaways from this report I put together, i’m going to share a few of them below, many of which are concerning in my mind, so I’ll start with the positive:

The good

• These roles are in super high demand, we went from 63 job postings total in 2024 to 3.3K+ in 2025 (lol).

  

• GTM Engineering delivers measurable revenue impact - 72% of respondents report measurable revenue impact from their GTM Engineering work. That’s not a “maybe” function - nearly three-quarters can point to real numbers.

• I think this data will enable GTMEs to advocate for higher salaries and importantly, more equity / performance upside based on the interest paybands we see in the USA in particular.

  

• A young, energized talent pool is forming - 54% of practitioners are under 27. GTM Engineering is attracting early-career talent, which means the pipeline is deep and the community will compound.

The Bad (and interesting)

• 49% of GTMEs receive no bonus for their performance

  

• 67% of GTMEs receive less than 10 bps of equity

• AI CRMs despite being highly anticipated by VCs and touted by believers in these markets were only mentioned by 3.5% of GTMEs

  • The same trend goes for Unify (8.8% of respondents), likely because they have swung more to the BDR workflow.

• There are (unsurprisingly) 25+ tools in the Most Exciting New Tools section that I have never even heard of.

  • There will always be another GTM tool so long as the sky is blue

    

• Unify has 8.8% usage but the largest request for a tool is an “All In One Outbound Tool”….. funny - who is cooking this?

  

• “Better / More Data” is another top requested feature → I think this speaks to the democratization of existing data sources and compression of alpha to be generated. “Reporting” and “global revops” are also mentioned → pointing me to an idea i’ve always had on creating a visibility platform centered on giving folks visibility across all their tools

GTME is a mispriced asset that I think will reach some equilibrium state over the next few years :)

An interesting theme - loneliness

Something i’ve been particularly surprised by in my 2ish years of doing this now is how lonely I can sometimes feel as a GTME.

The data shows nearly 55% of GTMEs say that their teams do not know what they actually do.

This is really sad → we have not yet articulated as a field the value we bring, how we do it, and why it matters.

It’ve always felt that doing GTME work has in a way, felt fairly alienating, and due to the cross functionality of the role - leads to GTMEs being responsible for a lot, reporting to everyone, and not being recognized for anything.

“If things work, no one notices. If they break, everyone blames you.”

- 2 GTMEs I caught up with over the last few weeks

It’s a tough role to take on, and i’ve seen many others feel the same pain.

I had a call last week with an early stage GTME friend who is juggling the requests of their boss (CEO), and that of their sales team (AEs / BDRs) while also trying to hit their own goals.

This rings true when you look at Top Bottlenecks - the top bottleneck is Bandwidth / Capacity. Folks are struggling to find the time of day to be cross functional with all they have on their plates.

Additionally, “Growth” teams tend to be smaller group of highly functional and high agency folks. I certainly felt this at Unify, it was a team of 2 of us for the majority of the time I was there, holding up the weight of the world…..

Questions for next time

There’s a bunch of things I want to query next time via this survey -

• What types of jobs are folks coming from?

• Deeper detail on tools folks use

• Exploration of non-outbound focused work (inbound, SEO/AEO, UGC / content)

• Discussion of internal built tooling versus bought SaaS

• % of workflows run with zero human touch versus some

Closing Remarks

In closing, I had a lot of fun putting this together. GTME as an asset is clearly mispriced and there’s a lot of noise about what a GTME is. But I think the role will begin to standardize, and we’ll see clearer comp bands, better equity packages and more for the folks that support revenue orgs behind the scenes.

If you’re lucky enough to have one, thank your GTME (and revops) folks today

Garrett

PS. If you’re in the market for a new job and youre a GTME - hit my dms, have a ton of good folks looking to hire!

In the last few weeks, Happenstance went from 100K→200K users…. that’s insane.

The AI Acceleration Era

It’s been a fascinating time to build over the last 2.5 years. While the ChatGPT most people know dropped in late 2022, it feels as though things didn’t take off in terms of shipping speed until later in 2024. This was when we began to see teams like Cursor raising multiple rounds in just a few months time, and dozens of other tools absolutely flying off the shelves and raising subsequent rounds one after the next.

This growth felt more akin to viral consumer / social growth than B2B SaaS. So why is that?

Well, at it’s heart - these tools go-to-market motions involve product-led-growth (PLG) (or are a massive portion of it).

Cursor, Juicebox, Clay, PostHog, you name it - they all have free tiers for their products that get people hooked; and just when you are excited to run your next workflow, they toss in the paywall and convince you to pay for your first month :)

And obviously @alex_teichman from Happenstance shared their user growth….. mind boggling.

This product-led growth (”PLG”) is what makes these companies blow up so fast and it’s pretty intriguing that this type of growth is really only possible for PLG focused companies. The revenue is less sticky, and the ACVs are probably not as large, and enterprise sales are tougher; BUT, damn it must be cool going from 10 users to 1000 users in a matter of days or weeks. For top down sales, this takes months, or years!

For many companies, its not a matter of if but when. Companies build a strong top down product with initial design partners and early customers, and when they believe the opportunity is large enough and onboarding is simple enough, they build a free version of the product.

I will caveat the “simple enough” notion though; Clay is far from simple, and somehow they got a very picky and opinionated crowd of GTMEs to try and use this tool despite how difficult it is… Was it peer pressure? Was it curiosity? is it FOMO?

The companies of the future will most certainly have a large PLG component - where folks can sell to, build for, and connect with directly to the end users who will act as internal champions. Executing GTM for this motion is fundamentally harder (esp as you move upmarket and want to sell to an entire org), but it’s what enables this hockey stick growth in part!

Product-led growth is so powerful because it gets individual folks to try the product in an easy to onboard way, but moreover can

• rapidly create product evangelists

• organically enable for user-generated content, discussion in forums, and more

• lead to user conferences, etc.

PLG is that super powerful lever for organic growth.

What PLG Actually Is (And What It’s Not)

Product-led growth isn’t just slapping a free tier onto your pricing page, it’s designing your entire go-to-market motion so that the product itself is the primary engine of acquisition, activation, and expansion. When you speak to teams who have nailed this, they spend hours upon hours upon hours with users, going extremely deep on customer discovery.

Clay literally made agencies the corner piece of their GTM to get them to build their businesses around their product. It’s brilliant, but moreover takes a certain type of intentionality.

I have spent dozens of hours on the phone with other Claygencies in random corners of the internet, learning new ways to do things and being excited by my client’s excitement for the results. (s.o and GTMCafe.com). We all help one another, its a beautiful thing honestly.

It’s working with users, its making the product fun, its optimizing for conversion, it’s gamifying the experience, etc. It’s creating community that talks organically

Why PLG Explodes So Fast in the AI Era

PLG explodes in the AI era because individuals, not committees, are now the true distribution layer for modern software. Everyone has a Ramp card now (or Rho, i’m a fan).

People have been given more initiative to go forward and do their own procurement process and move faster since everyone else is doing it.

Innovate or be left behind, and tools that require a demo thats booked 2 weeks out will lose a battle to a competitor who can let you login yesterday :)

The Hidden Tradeoffs of PLG

As intoxicating as that 10-to-1000 user jump can be, PLG comes with real tradeoffs around revenue stickiness, ACVs, and the complexity of moving upmarket. Just as easily as a user can join, that same user can vanish, and they can tell their frienda about it too!

It’s harder to give support to disparate individuals than one enterprise, and it certainly is more difficult to collect the credit card numbers of 100 people rather than manage it once with centralized billing. Moreover, the access controls and permissions are tough to nail here :)

PLG + Sales: The Hybrid Future

The future isn’t PLG versus sales, it’s PLG creating internal champions first and sales stepping in later to scale that usage across the organization.

Given the vast amount of data today and AI to be leveraged alongside it, I think PLG will have an even stronger and brighter future than it did a few years ago (in reference to Calixa shutting down, and Pocus moving away from PLG). I’m excited to see what people hack together or build into a SaaS here.

To that end, individuals can use AI to analyze usage data, understand trends, and help find buyers primed for a enterprise contract. This is the future, giving your idea wings with PLG, and then getting the whole flock with a proper sales process.

The Bigger Picture: PLG as Cultural Shift

At a deeper level, PLG isn’t just a growth strategy, it’s a cultural shift toward respecting the end user as the starting point of every go-to-market motion. Every person has limited attention, limited time, and above all - a opinion about what they want to do and how. They have buying power and they have intent to act with more agency than ever before.

Empower them.

Products that give them the keys, excite them, and provide value that is easy to understand will create some of the word’s greatest companies.

Go out and conquer!

It’s been really interesting having left Unify and seeing the wealth of other available tools out there for revenue teams. I’ve personally spent time in a handful of newer tools (SixtyFour.ai, Origami) and am now revisiting others I stepped away from while buried in Galaxy work.

Over the last few months i’ve gotten a tremendous number of questions on Clay vs Unify. So I wanted to write this to give those who don’t know Unify or Clay a view outside-in of what they can be used for and where each one wins.

We never really know why we’re better than our competitors

I find it particularly interesting that in work, as in sports, as in life - we’re ultimately battling opponents day in and day out; however, we rarely see things from the opponent’s point of view.

I used Unify daily for 13 months while I was there in 2024-2025 and I’ve now used Clay daily from early 2025-to today. I’ve also implemented Clay across dozens of teams and have made recommendations on how to use both - there are likely few other people who are better suited to write such a comparison.

And of course keep in mind that this is but a small screenshot of both tools today January 2026. This is not a feature-by-feature teardown. It’s a comparison of how these tools think about work.

The Real Tradeoff: Systems of Action vs Systems of Data

Unify and Clay are very different tools. They’re used for many of the same things but they go about it in completely different ways.

Unify is an action-oriented platform that prioritizes outbound execution, while Clay is a data-first system designed for observation, iteration, and reuse.

Where Unify focuses on building an action oriented HQ for conducting outbound for BDR teams, Clay is a surgical observatory where you can collect data, manipulate it, and choose what to do with it. Your two choices are outbound fast, or data then action slow and steady.

Almost every difference between Unify and Clay is downstream of this tradeoff.

How Work Actually Gets Done in Each Tool (Plays vs Tables)

Unify

Unify was built first and foremost to be an outbound tool. While they started with GTME outbound automation, they’ve headed more down the BDR tooling route since Q2 2025. Unify is essentially ripping and replacing Apollo/Outreach/Salesloft at breakneck pace because of how slow those players are to innovate and because of the clunkiness of their 2000s/2010s UIs. The platform allows teams to centralize intent data (and see a historical view of intent data too in one “Activity Feed”), find folks at various accounts for you, and then create a backlog of emails (tasks) that BDRs can write without leaving the platform. It makes it easier to be a BDR.

That should tell you a lot about what the team is building right now → A BDR automation tool with some of the tooling for GTM engineering automation. Unify is very much a system of action for BDRs who want to see what accounts are showing intent.

Unify’s workflow revolves around what it calls a “Play” (a workflow) based on some type of trigger such as intent (website visitors, Linkedin followers) or other sources (CSV upload, etc.).

Using a Zapier like interface, you can build a workflow based on set logic, AI qualification, prospecting for people, and ultimately outbound campaigns or “Sequences” as Unify calls it.

You start with a set of Companies, use AI to do some qualification, Prospect for People (and enrich) with certain titles, and then enroll them into sequences.

This allows you to quickly create a set structure for how outbound should run in set environments under certain conditions.

Much of Unify’s speed comes from how opinionated its prospecting and qualification model is. Companies and people are surfaced as CRM-known, Unify-known, or net-new, with simple title inclusion and exclusion logic that trades nuance for velocity. Reusable components allow teams to update workflows globally without rethinking the system.

Unify is geared towards BDRs and optimizes for outbound execution speed and set workflows

Clay

Clay on the other hand is the love child (as I say) of Excel, LinkedIn, and ChatGPT. It is a tabular interface that allows you to conduct incremental operations on data such as data enrichments, AI analysis, and even lookups in other tables. Across any given client I work with, I have dozens of workbooks (a collection of tables) and dozens of table lookups, CRM lookups, and more.

For example I might start with a company list, pull in 5 sources of data, normalize all of them, score, them, and more all using the table interface. I might reference other data tables and data points, and more.

There are hundreds of integrations and the tabular experience allows you to step thru your “idea” for growth, creating one column at a time, and work thru one step at a time.

This table-first approach is what enables Clay’s power. Data can be reused across tables, enrichments don’t need to rerun, and APIs are first-class citizens rather than add-ons. That’s why Clay often ends up being used for far more than outbound, from scoring to recruiting to TAM analysis.

Clay optimizes for reusable data and highly bespoke, piecemeal workflows.

Where Unify Clearly Wins (and Where It Doesn’t)

As a GTME tool, Unify wins for teams that want to leverage intent data to do outbound but don’t want complexity and don’t care about data configurability / Revops as much. Given its a ton easier to set up and less daunting to troubleshoot, it’s far easier to spin up here.

As a BDR tool, Unify has a beautiful interface that modernizes on top of much of the functionality that Apollo won over BDR teams with years ago. Much of the work that I spend building Views for in Hubspot or other CRMs is somewhat out of the box in Unify’s Audience views - meaning teams can see where and when intent took place (though Unify doesnt make it as easy to view custom CRM columns within Unify. Unify believes there’s no need for external data sources, or multiple tools, it can and should all live in Unify. Not needing to deal with 4+ external tools is part of the appeal of Unify (but also one of the downsides). Is the quote “jack of all trades, master of none” true?

Cost sensitivity, data quality prioritization, lots of integrations, 3P APIs → these are areas that Unify does not take the trophy in today.

Where Clay Clearly Wins (and Where It Doesn’t)

As a GTME tool, Clay is a dream platform for technical GTMEs. Complete cost control with external integrations, piecemeal and iterative operations, cross workspace/environment lookups, and more make Clay a must-have for GTMEs looking to focus on data cleanliness, data validation, and ultimately agility in building GTM workflows.

Even though Clay is building rep tools this year, I simply can’t imagine they build this in a tasteful enough way that BDR teams use it and like it. It’s just too complex of a product and I think itll be hard for them to switch from technical personas → way less technical.

GTME’s love it because of its configurability and the driver’s ability to optimize for control, cost, and reuse of data.

But at the same time, for many folks Clay is overly complex, is difficult to onboard a whole team onto and share ownership, and also has a weaker prospecting engine. When I first started using Clay I was simply overwhelmed by all the complications in workflows that Unify to an extent has simplified.

Why These Tools Will Never Converge

As I mentioned, Clay and Unify are built for two different types of people. And while I think everyone wants to to be the “categorical winner”, I don’t know if either tool will be able to satisfy both sides of the market - notably, technical and non-technical.

At most companies, Unify is owned by sales leadership and operated daily by BDRs, while Clay is owned by a single technical GTME or an external operator who becomes the system’s steward.

These products are opinionated not just in features, but in who they believe should be doing the work.

They both will try to overtake the other, but I feel as though the way to truly win is to stay extremely opinionated not only what you are building, but who it is intended for.

Just as Clay’s API and integration features will never be useful to a BDR, Unify’s quick account research and “tasks” will never be useful to a GTME.

The end

I’m including my bulleted notes on both platforms below that go into further detail if you’re really curious, but this is where the meat of the substack post ends :)

GW

Appendix Notes

Unify

• TLDR: outbound, simplified, fast

A lot of the secret sauce in Unify’s workflows come down to a handful of key areas

1. Prospecting - finding people and companies

   1. This is one of Unify’s standout features, focused predominantly on surfacing people who exist in a few different categories:

      1. exist in your CRM only

      2. exist in Unify (Unify has “found them” before)

      3. exist in neither Unify or CRM (net new)

   2. Title Filtering - contains a beautifully simple inclusion / exclusion logic in a nice format. Though this lacks an AI component / reasoning which would be a great incremental improvement

2. Plays - Fairly easy to spin up each signal in a visual workflow canvas where you can see the movement / qualification of leads.

   1. Fairly fast time to spin up an MVP as it feels akin to other tools and everything from signals, prospecting, data to outbound sit under one roof, making it easier for your team to get a centralized view of all the intent that exists for your company

3. BDR focus

   1. The BDR tool was born out of customers asking for a newer version of Apollo for their BDRs. BDRs spend time in Apollo making sequences, then ultimately explore Linkedin all day and drop folks into sequences using the Apollo Chrome Extension, which is highly manual

   2. Unify basically said lets reimagine this in a more modern way. We’ll centralize intent data with Unify, find folks at various accounts for you, and then create a backlog of emails (tasks) that BDRs can write without leaving the platform. It makes it easier to be a BDR but I feel as though this too will soon outdated and if BDRs continue to underperform long-term, this will become less valuable

   3. Single Pane of Glass

      1. Unify has “Activity” views for each Company and Person so you can see what intent signals and outbound has occurred for each entity. For BDRs this is incredible to see when the last time someone was contacted, etc.

      2. This is very akin to making a “View” in something like HubSpot for reps.

4. Reusable Components

   1. Unify makes it very easy to effect change across all your workflows seamlessly, with reusable and easily globally updated Components (an area that Clay is beta testing right now with Functions).

5. Pros

   1. Fairly simple to setup (relative to Clay)

   2. Is liked (as far as I know) by BDRs as a modern replacement to Apollo

   3. Doesnt require AI usage to generate snippets (this may change and costs for this are soooo low via Clay but just flagging)

   4. Has a beautiful interface

   5. Solid reporting and dashboarding which is slow / difficult to use today but will likely get a overhaul in the future

   6. Truly unifies the stack across things like RB2B/Vector/6sense (website visits), Clay (workflows and signals), Warmly / Common Room (signals), Instantly/Smartlead/Lemlist (email), HeyReach (not yet but linkedin messaging soon I think?), making it far easier to manage for one or a few people

6. Cons

   • Additional Signals

     • Today Unify supports website visitors, new hires, job posting, Lookalikes, and any signal that can be gauged by a recurring websie search using AI (product announcements, event attendance, etc.).

     • They will be adding a ton more in 2026 however

   • No “table” experience.

     • This is an area that Unify will certainly change, but it does not have a table experience. This means that while the product is easier to use, its difficult to store, retain, and reuse information across the Unify platform. This is one of Clay’s strongest features.

   • Lack of APIs

     • As of this writing, Unify still does not have an API to pipe in and out data from other sources. CSV, Unify intent, and CRM are the only way to get data in and out. I believe it’s only available in Beta to Enterprise customers

       • Concretely, this means all enrichments and funky signals / imports / export require usage of Unify Credit’s rather than using external APIs which might be cheaper all-in.

     • Thus access to other email tools, custom API calls, etc are all not possible.

   • No Scoring

     • Clay has a decent scoring UX, but Unify is more binary here - a Company or Contact is or is not qualified.

   • Miscellaneous

     • Agent outputs can not be formatted into highly specific JSON

   • No ESP matching

   • Has to include unsub links, no “respond pause to remove” without a link

   • Harder to edit email formatting / copy

   • No unified inbox experience yet

• Price Point

  • $25K/year for base plan

Summary

Unify is an exceptionally gorgeous product that serves teams who don’t care about the price tag with intent led outbound super quickly. What it lacks in customization (and complexity), it makes up for with simplicity and provides a modern BDR experience as well. It is truly a reimagination of an outbound tool, purpose built for BDRs and single GTMEs, but lacks the functionality for real GTMEs or Claygencies to use effectively.

I recommend this for folks who want to get their outbound up and running as fast as possible in a straightforward way without a full-time GTME who will spend all day optimizing their outbound. Best fit for BDRs who need a bit more hand holding

Clay

• TLDR: A data nerd’s best-friend for reusable data and GTM workflows

Now before you read into this too much, keep in mind that Clay has been around far longer than Unify. I am also biased but have used Clay for hundreds of hours (if not more) over the last 9 months. They have built an exceptionally powerful product for people that like staying up till 4AM to show their friends what they built. Sometimes I am terrified by how plugged in I can get because of how hacky it feels. This week alone I stayed up till 3AM a handful of nights experimenting with new workflows. Clay is an exceptionally complex product and will likely not ever be able to unwind that vibe, but I don’t think it should. It is a highly opinionated product on “data is core to GTM” and that is what makes it stand out. Yes, it is fucking difficult to use and sometimes I want to claw my eyes out, but wow is it powerful.

• Features

  • List Building

  • Large suite of 3P data sources

  • Table view with ability to conduct workflows “one step at a time” and see / test results before continuing

  • Highly configurable and ability to reuse data across tables such that enrichments don’t need to run over and over again.

  • Access to API calling

    • Making virtually any API accessible via Clay, making niche 3P signals and platforms easily within use

    • Can use AI for Complex operations like

      • Determining someones primary FT job

      • Cleaning names of people, companies, etc.

  • Has functionality that makes it far more than just a outbound tool - recruiting, scoring, even TAM builds, etc.

• Cons

  • Clay is not a product for BDRs as it is highly complex

  • The design and clunkiness of the product can be frustrating at times

  • In many areas it lacks organization and throws everything at you and expects you to sift through it (sorry)

  • Lacks in reusable components in my opinion (despite functions beta-roll out and within Claygents / otherwise)

  • Fairly steep learning curve

  • Poorer prospecting engine when compared to Unify (imo), lots of Claygencies use 3P prospecting APIs (Apollo, LeadMagic, etc.)

  • Owned by one person internally or a external agency / claygency

• Pricing

  • Cheaper by 2-3X.

Summary

Clay is an incredibly powerful product that has no intention on being one used by Sales people, rather it is intended to be used by folks with systems thinking, a desire for hackiness, and building workflows that can empower everyone from sales, to marketing, to hiring in various ways.

While Clay is priced at a cheaper price point, credit overages, the need for a user to configure and optimize constantly, and more make it difficult for team’s who have limited time or FTEs to work on this workflow.

If you want to be the hackiest GTM expert on the planet, Clay is certainly the right tool for you and I tend to recommend it for teams that want to build the most bespoke workflows

Takeaways

For team’s that want to do simplified automated outbound fast and have the $ to work with, Unify is a beautifully simple platform that will get you outbounding on your first set of signals within a few days (post-mailbox warming). I’d recommend it for folks that want BDR led outbound.

For team’s that want extremely configurable data enrichment, outbounding on a variety of different signals, and way more control of what they’re doing, Clay is the way to go. Though they’ll need the time, sanity, and patience to get there!

As always, ping me to get in touch for advice, advisory work, or implementation of your GTM needs, whatever they may be!

https://www.linkedin.com/in/garrettawolfe

getaob.com

But even without understanding the plumbing, one thing has become increasingly obvious to me.

A huge number of modern workflows reduce down to the same core problem: finding the right people, companies, or entities at the right moment.

It’s all Search

Working with Unify and Clay, I’ve seen firsthand that sales is, in large part, a “finding the right people” problem. Everything downstream - messaging, sequencing, deliverability, CRM hygiene - matters, but it only matters after you’ve identified who is actually worth reaching out to.

When I first spent time in the recruiting world and came across Juicebox, I had the exact same realization. Recruiting is also, in large part, a “finding the right people” problem. Different personas, different incentives, different workflows, but the same underlying motion: identify a narrow, relevant set of humans and then reach out to them.

That parallel stuck with me.

Then someone clued me into Exa, and more recently Parallel, and it pushed the thought further. Are Unify, Clay, and Juicebox actually thinking big enough? Or more pointedly, are we all thinking about these tools too narrowly?

Is it possible that sales, recruiting, partnerships, research, investing, corp dev, and a dozen other functions are all just different flavors of the same underlying workflow?

• Sales is finding and emailing people to sell to

• Recruiting is finding and emailing people to recruit

• Partnerships is finding and emailing people to partner with

• Investing is finding and emailing people to diligence

• Research is finding and synthesizing information tied to entities

This is a huge DUH; but truly → the differences are real, but they mostly show up downstream. The top of the funnel looks eerily similar.

What’s fascinating is that we’re already seeing some bleed-over. Juicebox has its own sequencing product that recruiters genuinely like. At the same time, they’ve very clearly not invested in deliverability infrastructure, which is honestly kind of hilarious. It’s a good reminder that what sales teams obsess over does not always map cleanly to what recruiters need. Still, the gravitational pull is there.

Why this idea failed before

The idea of “one tool to rule them all” is not new. Horizontal platforms have tried this many times before, and almost all of them failed for predictable reasons.

• They were too generic.

• They tried to serve too many workflows at once.

• They struggled to define a clear buyer.

And their interfaces collapsed under the weight of competing use cases.

Most importantly, legacy search was brittle. Boolean logic, rigid filters, and pre-defined fields meant you could only search for things you already knew how to describe. Discovery was constrained by the schema, not driven by intent.

AI-powered search meaningfully changes that equation. Intent can now be expressed in language instead of dropdowns. Recall improves dramatically. You can start from unknown unknowns rather than a static list of entities. That alone reopens a category many people had written off as impossible.

Exa Releases Websets…

As far as I can tell, Exa originally positioned itself as a developer-friendly way to call a powerful Search API directly inside your own product. In theory, platforms like Unify, Clay, and Juicebox could all leverage Exa’s AI search infrastructure instead of building and maintaining their own bespoke search systems.

But then something interesting happened.

With the rise of GTM influencers, creators, Claygencies, and a growing class of semi-technical operators who live in spreadsheets and tables, Exa released Websets. A Clay-like, table-driven interface for doing AI-powered search. That release caught my attention more than the API itself. And I think it caught many others’s eyes.

Tables are a surprisingly powerful abstraction. They let humans stay in the loop. They make iteration visible. They allow you to reason about search results, scoring, and enrichment in a way that feels tangible rather than magical. Websets felt less like “search as infrastructure” and more like “search as a workflow primitive.”

The Exa team gave me free access for a few months, and I’ve used it to build some genuinely interesting workflows.

First, very fine-tuned LinkedIn and data searches for companies or people, without needing to be an expert in legacy filters or memorizing which dropdown does what. You can express intent in plain language and then refine from there.

Second, news-driven and event-based data tied to companies or people. This is where it got really exciting for me. For example, searching for “manufacturing plants in the USA that produce paper, plastic, or metal that have shut down in the last 12 months.” In Clay, I’ve historically approximated this with RSS feeds, largely because I don’t know which companies even have that signal ahead of time. Using Exa-style search, I surfaced multiple cases Clay never picked up. That gap alone made me pause.

Third, more niche or long-tail data like recent research papers, obscure announcements, or signals that don’t neatly map to existing enrichment providers.

Why tables matter more than they look like they should

Websets is interesting not because it is flashy, but because it is boring in exactly the right way.

Tables are the natural interface for semi-technical operators. GTM teams, recruiters, and researchers have lived in spreadsheets for decades because rows and columns make uncertainty manageable. You can see results. You can tweak assumptions. You can stay in control.

AI wrapped in a table does not feel magical. It feels inspectable. Trustable. Editable.

That matters more than raw capability. Whoever wins the table-native experience often wins distribution, even if their underlying technology is not the most sophisticated.

My Questions

All of this leads me to a set of questions I can’t shake.

• Is Exa more excited about being an API company long term, or about Websets as a product? APIs scale quietly and defensibly, but products shape user behavior and expectations.

  • This is where things get interesting.

    • Search infrastructure wants to be neutral, invisible, and ubiquitous. APIs scale best when they power everyone quietly in the background. Products, on the other hand, want to own the user, the workflow, and the moment where intent turns into action.

    • Those incentives are fundamentally at odds. If you power sales tools, recruiting tools, research tools, and internal platforms all at once, staying neutral becomes harder over time. The moment users fall in love with a workflow built directly on top of search, the question shifts from “who powers this?” to “who should own this?” That tension is not about roadmap choices. It is structural.

  

• GTM users are notoriously needy, myself very much included. If you give them powerful search, they will inevitably ask for emailing, sequencing, scoring, collaboration, and all the other things Clay and Unify already do. Should Exa listen to those requests, or is that the fastest way to lose focus?

• Do they stop at search, or is search just the wedge? Once you own the entity graph and the discovery layer, the temptation to move downstream is enormous.

• And finally, if Exa already powers AI search for a growing number of platforms, do they see themselves as fundamentally complementary to GTM and recruiting tools, or as a future competitor wearing a thinner UI today?

A soft prediction

The more I think about it, the more it feels like we’re approaching a convergence point. My bet is search will become more commoditized, but high-quality, intent-aware search is and still will be rare. Workflows are differentiated, but increasingly depend on the same discovery layer. Somewhere in the middle, a new category may be forming.

The question is not whether these worlds collide. It’s who owns the moment where search turns into action.

If everyone eventually has access to the same AI-powered search infrastructure, what actually becomes scarce again?

Is it attention?

Is it trust?

Is it judgment?

Or is it simply owning the moment where discovery turns into action?

That answer probably determines which of these tools stay infrastructure, which become platforms, and which quietly disappear.

I wanted to use our first week back from the holidays to share some of my favorite tools and the folks building them. It’s easy to use tools without getting to know the people cranking away creating them, and for several of these I’ve been lucky enough to get to know the builders as well!

Notably, I have explicitly mentioned whether I’ve used the tool or not, so I’m not just shilling recommendations at you not having used it (like many other people do), and this is not sponsored in any way whatsoever, just doing my part to share the love and give thanks!

Inbound Scheduling & Workflows - Default

Default - Default is an incredible team building in the inbound space led up by Nico, Victor, Stan, George, Caitlin, and a handful of other exceptional individuals. For teams that are scaling and need scheduling, routing, and workflow tools associated with inbound demos - Default is a modern inbound workflows solution!

Funny, when I first started my journey into the startup world, I got to know Nico and the team and immediately fell in love. They weren’t ready to hire someone with my background at the time but I am so grateful to have stayed close to the team, become a power user of their product, and have implemented it for Unify and a handful of other clients! I’ve never known any different but I have used Hubspot’s calendar, Calendly, and am grateful for what they’re building.

Linkedin Signals - PhantomBuster & Captain Data

I feel like Phantombuster and CaptainData are some of the unsung heroes of GTM that I’ve had a ton of personal experience with over the last 24 months. These platforms can be used to scrape all types of engagement data on Linkedin like likes, comments, follows, viewers, as well as just general Linkedin scrapes, helping build lead lists on Linkedin among others. Before Unify built an integration with these tools, I personally used Phantombuster and CaptainData to scrape 50+ Linkedin engagement categories across thousands of people at Unify which generated milions of dollars of pipeline for us. The Phantombuster team (John Mears, Jackie Newnam, Brian Moran, Diane Bogui) and Captain Data team (Marc Francis, Chloe Damon, Olivier Darcq) are extremely kind, patient, and friendly - even with the admittedly steepish learning curve to work their systems overtime! God knows where i’d be without learning about these guys!

Favorite: My favorite is Phantombuster here for the friendlier UI, but CaptainData has a bit more power / configuration for folks who want to go really deep.

Website Deanonymization - RB2B / Vector

Although I’ve never spoken to anyone on the RB2B team, you gotta hand it to Adam on the Retention.com train for building a great media engine in founder led content by building in public and admitting both wins and losses.

Vector is competitor to RB2B that offers a very similar suite of tools with a team that has responded to support inquiries near instantly and their founding AE has gotten on the phone with me at a moments notice.

For deanonymizing website visitors (which could be considered both shady but also highly useful), RB2B, Vector, and a few other providers are great platforms.

Favorite: I’m a paying RB2B user, for whatever reason i’ve gotten better results from them but have heard of many friends who far prefer Vector.

Lead Gen Workflows - Clay and Unify

It’s unfair to give either of these too much airtime but both of these tools are category defining and revolutionary within the space. Unify’s team is comprised of some of the best specialists Hyewon Kim, Solomon Astley, Sam Waterbury, Skyler Mickunas (and quite a few folks who have gone out to start their own companies as well - Devin Plumb, etc.). Clay is no different, having met folks like Everett, Brett Fischler, Nick Goel, Daniel Johnson, Josh Hanson, Alex Lindahl, and many more - they are building something truly special

Clay is the power tool for GTMEs, and Unify seems to be going down the BDR automation (replacing Apollo route).

Favorite: Biased here and like both for different reasons :)

Email Tools - Instantly, Smartlead, Lemlist

Instantly, Smartlead, Lemlist, EmailBison - there’s a bunch here.

Instantly has the cleanest UI / UX but Smartlead has a better API endpoint. EmailBison people love for deliverability since its on different (I believe private) email infra but is newer to the game. Haven’t used Lemlist but have heard good things.

Basic Linkedin Messaging - Heyreach, LaGrowthMachine, Dripify

HeyReach has been my tool of choice for Linkedin messaging ever since a few of my clients introed me to it. They are all functionally the same and I don’t have a strong preference here but HeyReach has seemed to have the best external opinions on it. We used LaGrowthMachine at Unify and the team was very kind to help us with success, improve messaging, and generally just shoot the shit. Have heard good things about Dripify but haven’t had the chance to use.

Favorite: HeyReach

Advanced Linkedin Prospecting & Messaging - Valley

In the early days of Unify we viewed them more as a competitor, but they’ve come to be more of a complement in my mind as they have sticked to just Linkedin (as opposed to the full gambit for outbound). CEO Zayd Syed Ali is super kind, he has let me try out the tool and its extremely intriguing and has a stunning UI. Their head of growth (Shubh Agrawal) also is a legend and is friendly, and funny (important!).

AI Search - Exa, Parallel

Super fascinating category that I plan to write about but these tools (Exa, Parallel, Extruct) basically use AI to scrape the web for your search parameters. They can be used for super vanilla B2B SaaS use cases (folks that match X criteria, companies that match Y filters) across sales, recruiting, and more. However, i’ve found these platforms particularly useful for far more useful things like highly niche signal monitoring. I have a ton of fun with Exa because it complements my RSS feed work in Clay in a super neat way. The team at Exa is incredible, and while i’ve heard people rave about Parallel just as much if not more, I prefer to interface with a list like UI rather than a proper API at least for right now.

These companies are still very early, but it reminds me a ton of Unify when I joined in April 2024 as they iron out a few key things like

• Promoting the product and creating product-led growth and marketing content

• Building a partnerships program

• and a bunch more!

Product Walkthrough / Demo Software - Navattic, Supademo

Making folks get on the phone with an AE in order to buy can be intimidating and annoying, thus a handful of platforms have popped up that allow folks to build guided walkthroughs or demos. When I was building Galaxy, I setup Supademo for us as I saw it on Twitter and it was super easy to get up and running. Created a great “intent signal” for us and converted to a bunch of design partners that I sold for us. Highly highly recommend using, and I met Joseph Lee (CEO) at a founder dinner purely coincidentally and Fredo Tan (Growth Lead) via product dogfooding and the team is great.

Navattic is a bit more mature and has deeper product suite (today). Randy Frank is a Duke Alum and had Ethan Dursht on his early team (before he joined Unify!). Alisa Feng has also been extremely kind to me as I diligence new product ideas as well.

Favorite: Supademo for bootstrapped companies / quick builds and early stage Companies; Navattic for more thorough demo automation with integrations, sandboxes, etc.

Email / Linkedin Activity Syncing - Outbound Sync

A difficult part of building these outbound systems is syncing everything back to your CRM so your team knows whats going on at a glance. For god knows what reason, none of the email tools / linkedin tools sync activities back to CRM. I’m very curious as to why they left out such a logical thing so if someone could give me an answer I would be plenty interested to hear. OutboundSync finally syncs back all of this interaction data directly to your CRM along with great dashboarding to manage what is going on. In this regard, they do compete with Unify somewhat which is fascinating. Harris Kenney is super friendly and a great guy to watch as he built this product mostly himself out of pure struggle and frustration.

ScaledMail, ZapMail, etc. - Email Infra Management

Managing email infrastructure is not only annoying, but complicated. Everyone has their own preference but I took the jump and chose ScaledMail to use which I have now rolled out over 4+ clients. They are super great for spinning up and down mailboxes, checking email health, and for just asking questions to. Because they deal with so many mailboxes, they can buy them at a cheaper cost then you and me buying our own via Outlook and Gmail. DNS settings and such can be irritating for folks, this eliminates that entire process and puts them in charge. Their team is also incredibly responsive, helpful, and always down to chat about things regardless of what they may be! Dean Fiacco is a great team lead here

Favorite: ScaledMail

CRM - HubSpot, Salesforce, Attio

I am not the most qualified person to speak on this subject but have used Hubspot, Salesforce, and a handful of others in the last few years. As Vic Schwenoa once put it to me:

• Attio can be used for simplistic CRM implementations for small companies.

• Salesforce if you want to build a trillion dollar company

• Hubspot can be used for everything inbetween.

Hubspot is what I use personally and typically recommend to folks. It’s a lot faster, snappier, and cleaner than Salesforce. This makes a big difference for deep revops work, for list building, and for actual utilization by reps and other teams which matters a ton.

Dharmesh Shah is also a masterclass in founder-led marketing, he posts on Linkedin and Twitter nonstop with very genuine and sincere content and Hubspot in particular is far more well rounded in other areas of operating other than sales engagement (marketing, CS, signals, etc.)

I’ve heard great things about the Octolanes, Clarify’s, Lightfields, and other AI CRM’s of the world, but have not used them yet simply out of lack of integrations with GTM tools. Excited to see how this newish space evolves

Favorite: Hubspot for ease of use and scalability

Contact Data - Wiza

Wiza has somehow appeared on the market as a new player that has somehow made it big, entirely bootstrapped. A really impressive feat. I’ve procured them for a client and have been impressed by the match rate of contact data versus other vendors. While they dont have a sequencing tool (yet), it’s nice to see someone crushing in the data space. Great folks here like George Uhlrich, Michael Kilcullen, and KJ Chow)

BDR Dialing - Nooks, Orum

I am going thru a buying cycle with Nooks and Orum right now. Anthony Firenzi recently started at Nooks and he tends to have a good nose for GTM tools so excited to see what comes here. Orum is another solid tool folks recommend, though I haven’t tried it just yet.

Free Contact Data + Email Sequences - Apollo

This is just such a no brainer but everyone should have the Apollo chrome extension and get their 100 free emails per month + 1 free email sequence. Just so awesome to have on hand and one of the best things to have while you bootstrap your ideas :)

AEO / GEO (&SEO) Optimization - Gauge

Transparently, I ran into the Gauge team by first trying to sell Galaxy to them and get them using it. I was already building AEO / GEO / SEO infrastructure myself in-house, and wasn’t interested in buying Gauge. What was initially me trying to sneakily sell, turned into a beautiful relationship with Caelean Barnes and Ethan Finkel showing me their platform, letting me use it, and seeing the trajectory of Galaxy go hockeystick as I invested more in building AEO / GEO. Track mentions, visibility, where to go get citations and more. Doesn’t hurt that they’re friendly (and I have a fairly active text chain w them).

AirOps (also love but haven’t used!) and Profound in this space too.

Notable Mentions

• SixtyFour AI - like n8n and Unify / Clay had a baby. Interesting for list building and lightweight signal orchestration → Chris Price

  

• Floqer - like n8n, Unify, and Clay had a baby. Anastasia Liamets, Shivam Mahajan

• n8n - obviously complex but powerful

• Origami - Cursor for outbound?

• Warmly

  • There are a lot of warm outbound tools out there (Unify included) and Warmly is one that comes to mind here. Max has a younger brother (that went to Duke) and has similarly done a great job of building in public (or as he calls it “going clear”). He’s built a lot with a very lean team and has given the GTM space a lot of good laughs and leans into it too :) I haven’t personally used Warmly but have heard good things from folks who want a simple automated outbound platform for warm signals like website intent.

• Keak - super sick AB testing platform

Tools I want to checkout in 2026

If you work at these companies and wanna give me a spin of your tool (or are a new entrant to the crowded space of GTM, hit me up)

• 11x

• Rox

• Actively and Amplemarket (need to know how yall compare to Unify ;))

• Cleanroom - Elias Stravik

• Seam AI

• Granola - finally time for me to invest here

• Mutiny

• Pocus - despite being adjacent to Unify, i’d love to learn more about their PLG focus and check out the platform - seems like a great product.

While deliverability relies mostly on email infrastructure, visibility relies mostly on how systems are connected and how we can look at them.

Deliverability

Ah the black box that is email deliverability. It’s fascinating that nobody has it really figured out and theres very little objective documentation on it. There a woman Grace Hopper who I think helped sign the CAN SPAM act but it’s strange we haven’t seen more technical analysis here IMHO.

Questions I continue to ask myself about deliverability (which @benyamin and Kellen Casebeer continue to tell me to simply ask Cursor), include

• Is ESP Matching dead?

• Can I warm my personal emails? I dont want my email to my mom to go to spam.

• How do i effectively test email experiments other than just A/B testing within the email platforms

• Will AI cannibalize and eviscerate spintax as time goes on? (Unify still hasnt launched spintax)

• Does it matter if I buy my mailboxes from Google / Microsoft direct, versus ScaledMail / Zapmail, versus thru Instantly

• How many links can I put in an email?

• Has anyone figured out how to rotate domains / mailboxes and optimize sending performance outside of just killing poor performing sequences, mailboxes, and domains? Why the heck hasnt Instantly or Smartlead built this?

I feel like people have guesses to all of the above, but no one has really solved it definitively with numbers.

It seems like the timeline of email in society went something like

1. Email was invented, people sent emails for business and work purposes

2. People figured out email could be a new sales and marketing channel

3. People started sending tons of sales and marketing emails

4. ESPs get smart and start introducing Spam filters and knock deliverablity for consistent violators

5. People come out of the woodwork experimenting with thing and call themselves deliverability gurus that are mostly in-house operators

6. These in-house operators leave and go start agencies and platforms that manage email infra

7. We still only have privatized data sources and run closed off experiments and reference anecdotes of how to avoid spam / best practices but there is nothing public.

8. No definitive way / solution as deemed by the market to allow mailboxes to “recover”, optimize domain and email rotation, etc.

At Unify we always wanted to have a backlog of emails to be sent, and assuming we sent the max number of emails one mailbox could send every day - deliverability will naturally drop. As time went on, the priority to fix this, but I didnt know how to fix it, let alone interfacing with our actual product backend to fix this.

While i’ve been doing growth contracting for a number of clients, I set out to try to solve this very specific problem.

Let’s assume we have 50 mailboxes, each sending 25 emails per day. That’s 1,250 emails per day, 6,250 emails per week. That’s roughly 26,250 emails per month. Turns out, this actually isnt that much (lol).

You are sending emails across 10+ different campaigns - some are high intent (some tried to book a demo but didn’t), some are medium intent (someone liked your post on Linkedin or downloaded your dumb lead magnet post), and some are straight up cold AF / frigid (the recipient is a human, has knee caps, and has an email address - there is no reason you should be emailing them lol). Without any type of optimization, all of these campaigns are being split across all email mailboxes equally. This isn’t that intelligent. We really should be doing a few things:

1. Send high intent outbound from mailboxes with peak deliverability

2. Send low intent outbound from mailboxes with lower deliverability

3. Constantly try to improve the overall deliverability of said mailbox “groups”

4. Occasionally let some mailboxes rest / “recover” and improve their deliverability

When I first joined Unify, I had to click every mailbox by hand that I wanted to add to a sequence. Me clicking my keyboard 250+ times (no exaggeration) to launch an email campaign inspired someone to build email groupings in the product…

I’ve spent the last few weeks building something in Cursor, Notion, and Instantly’s v2 API to power a lightweight reporting mechanism and infrastructure for this, and am super excited to have it up and running.

This image is an abstract representation of the tool I’ve built.

1. Pulls all the mailboxes for a client from the Instantly API and checks their reply rates and health scores over the last two weeks if it has sent a minimum threshold number of emails.

2. Sorts all mailboxes by a descending weighted score that combines reply rate, bounce rate, email health score, and number of sends.

3. Proportionally assigns every mailbox to one of four mailbox groupings based on its ranking

   1. High - our highest performing mailboxes for our highest intent sequences

   2. Medium - medium performance for medium intent

   3. Low - so on and so forth

   4. Recovery - The lowest performing mailboxes in this group will not send any emails for the next two weeks and then we will reintroduce them to the sending pool

4. We now resume sending and monitor reply rates every 2 weeks, repeating the above steps

5. Tracks performance of mailboxes by group and individually every two weeks such that we can see trajectory.

What am I missing from this configuration? What should I add? Would you subscribe to a product like this if I sold access for like $40/month or a price per mailbox?

Visibility

The second area i’ve been thinking a lot about is visibility, not just into what’s working, but where things are going wrong, how to find them; and most importantly, how to fix them.

Building across a disparate set of tools ala Clay, Instantly, HeyReach, OutboundSync, etc results in GTME’s needing to track down why a email was sent with the wrong variable, why a email mailbox was disconnected, why an enrichment failed or errored out. With Clay in particular, I constantly need to sift thru dozens of tables to identify where the problem is and how to fix it. Annoyingly, sometimes I run out of credits and I’m not told which tables have been affected and when I buy more or get my credit top-up, I don’t know how to resume those failed runs without doing so manually.

This feels like a problem, and one worthwhile to fix (albeit it may not be a product in itself but rather a strong feature for folks who like to get in the weeds and manage these systems).

I imagine a world where there is a central mission control where GTMEs can see health focused notifications such as

• Enrichments that were cut short due to insufficient credits

• Leads can be tracked globally in each platform but also overall (so I dont need to login to all of my tools simultaneously)

• APIs hit rate limits or errors

• credit spending has jumped in areas with low historical usage

• and so on and so forth.

I think this would be a GTMEs / Revops’s dream.

….

Is anyone out there making this or email / domain rotation a reality?

Let me know your thoughts below :)

As I’ve gone earlier and earlier in the startup world it’s been rather impressive to see just how many teams are generating millions of dollars of ARR and don’t have or use a CRM (customer relationship management) consistently. It’s actually mind blowing.

So much of the modern tech stack plugs into your CRM these days, from GTM tools, to finance tools, to tons else. Certainly you can make the argument that at the earliest stages, it is more important to focus on product velocity and output than focusing time and effort on a glorified database, but I just laugh every time I meet a 1M+ ARR company without a well ironed out, or at least light weight CRM instance they are using.

I think it’s a huge red flag if deals are living only in founders’ heads or their DMs.

Before I joined Unify I started following a bunch of the Instagram meme accounts for sales / GTM humor. I’ve never been an AE / BDR so I wouldnt know what its like to be a commission earning rep but these memes are hilarious. All the memes were about reps forgetting to, refusing to, or not knowing how to update CRM. I can understand it to an extent: you’re so focused on generating business and closing your book of business that you don’t necessarily want to prioritize contributing to a bunch of “seemingly” arbitrary documentation.

But making the CRM your source of truth is so important. For day to day operations and managing deal flow, to helping any individual instantly know what the latest status is with an existing account, to learning the different types of accounts that your Company works with (Customers, Competitors, Partners, etc.).

In this little post I’m going to just share a few of the reasons I love using CRMs for record-keeping, why you should to, and some of the minimum things you can implement to know you’re doing the very bare minimum until you hire a revops / bizops person in charge of making it more official :) And if you are looking for that person, let me know, I know a handful of awesome folks!

What a CRM actually is

If this is net new information to you or you aren’t skipping this section, you’re part of the problem 🤣

A CRM is just a structured place to store four things:

• Who you are talking to (Companies, Contacts)

• What is happening with them (Deals / Opportunities)

• What has happened (email, notes, calls, meetings)

• What is supposed to happen next (tasks, next steps, dates)

Why to Implement

It’s no wonder Salesforce and Hubspot in aggregate have a market cap of >$240 Billion. So why should you start using a CRM from the earliest stages?

• Well first off a few convenient features

  • Hubspot and Attio both have generous free plans

  • They help you track emails and send templated emails very easily

  • You can get free calendar links thru them

  • These are all no brainers for someone looking to start building that Revops repore but also for recurring day to day ops you need for your biz

Onto the more juicy stuff:

• Integrations: As you scale there’s so many things that plug into your CRM, from outbound tools, to call recording tools, to inbound tools, etc.

  • You are missing out on all the functionality and leverage these systems give you

• Record Keeping & Access to Data: As you hire, anyone customer facing will need to see what the latest is for any customer notes, open opps in the pipeline, reasons for closed lost opps, the nature of your competitors, and more. This makes onboarding faster

• List Building: The criteria you use to segment Companies and Contacts is so important! Whether its making a list of Customers, Competitors, Partners, etc. that the whole company can refer to. Or segmenting by industry / other fields!

• Reporting: This is the real reason teams need CRMs, for reporting. Because most founders don’t give up a board seat prior to the Series A, this tends to take a backseat. But really you can make your sales process repeatable by being highly analytical and data-driven in how you evaluate your pipeline and the resulting funnel.

  • How are we performing relative to goal?

  • Which campaign source for opps has the great closed won conversion?

  • Which opps are stalling and we haven’t followed up with

And dozens more

When should you start using a CRM?

Ideally? Yesterday. The moment you have more than 5-10 active accounts you’re having conversations with or have an actual AE outside of the founder.

The earlier you start, the better off you’ll be. And if you use Hubspot for marketing / newsletters, this means you can mass email your friends, family, and customers earlier and with more consistency than you might otherwise.

Which CRM should I get?

There’s tons of new CRMs every day. The core ones I know / rep are:

1. Salesforce - buy if youre going to build the next multi-billion dollar company

2. Hubspot - buy if you want to maybe build a billion dollar company or if you want people to move far more effiicently when using CRM. This is my typical recommendation

Hubspot and Salesforce are most folks’s typical recommendations because not only can big companies be built on them, but they also have solved some large RevOps problems over the years and also have the most thorough integration potential.

1. Attio - buy if you hate CRMs but want something far easier.

2. AI CRMs - new age AI CRMs, havent seen these be used by large companies to date.

   1. Octolane - Duke founders!

   2. Lightfield - from the Tome team

   3. Clarify.ai - have heard great things

   4. Day AI

Build your MV-CRM (Minimum Viable CRM)

There’s only a handful of things that I think, if you can do, puts you in a great place.

1. Add all the Companies, People, and entities to your CRM that you’ve interacted with to date.

   1. For Companies, categorize them using a “Type” field into Customers, Competitors, Partners, Investor, News/Media, etc.

   2. For Contacts, to the extent you can, associate them with the proper Company and then categorize them using a “Persona” field into one of your various business personas (Sales Leader, Marketing Leader, etc.), and create a few extra ones for random folks that you engage with like “Friends”, “Family”, “Investors”, etc.

   3. Down the line, this will help you with the first 3 bullets in the previous section

2. Decide what your sales process looks like in CRM.

   1. Deal Stages: Pick the “stages” you will use and don’t overcomplicate it more than necessary.

      1. Meeting Booked, Opportunity Qualified (this would be post the first call and they express continued interest), Discovery (deeper exploration of the use-case), Negotiation, Contract Sent, Closed Won / Closed Lost

         1. Some might even say the above list of 6 stages is too many. Don’t do too many, the more hurdles you add, the less it will be used and the more misunderstood the process will be for other folks on your team

         2. There should never be a deal created unless a Company is in an active deal evaluation process

      2. Deal Details: Identify the fields that you care about for each deal

         1. Internal Owner - Who is the person internally that is responsible for owning and working this deal. Is it a founder, an AE, etc? This is super important for accountability

         2. Campaign Source - This is sadly such a slept-on field. Where did this opportunity come from? Make this a set picklist of a select set of options. Inbound, Manual Outbound (BDRs), Automated Outbound, Referrals, Events, Conferences, Paid Traffic , etc.

            1. This will allow you to see a breakdown of where opps are coming from every month!

         3. Use Case - Make a multi-select field that contains the primary reasons the prospect is interested in your product. This will help to cater your comms with them when you make updates to certain parts of the surface area

         4. Plan Type - Create a picklist of plan types so that you can pivot opportunities by the size / type of deal

         5. Amount / Contract Value - this is a default field but good to always put a placeholder value

         6. Decision Maker - Once you identify the Person who is a buyer / decision maker, you can keep them in this field.

         7. Closed Lost Reason - This is a key learning field. Why did you lose this deal? Was it for budget reasons? Was it to a competitor? Did they go silent / ghost? Are they too big / too small? Was the pain not acute enough. Pick a list of your top 5-7 reasons here and reflect them.

         8. Next Step - This is what powers “what do I do today” views and stops deals from just sitting in “Discovery” forever.

         9. Pipeline - A way to categorize what “deals” are closable sales opportunities versus which opps might be better classified as opps for Partners, Affiliates, etc.

3. Standardize and document this process and share it with your team.

   1. Write and build docs with the process you want to use, and then provide it to the team and make sure they adhere to it. Good data = good company! The motto I like is “if it isn’t in CRM, then it didn’t happen”

   • Every deal must have:

     • An up to date stage

     • A clear next step and date

     • An internal owner

   • Every week:

     • Reps clean their pipeline: kill zombie opps, update stages, add notes from calls

     • Founder / sales lead reviews:

       • Top deals this month

       • Slipping deals (close date moved multiple times)

   • Every activity:

     • Log notes in the CRM, not in personal docs

     • BCC the CRM email or use the Gmail / Outlook plugin so every email is attached to the contact and deal

4. Build lightweight reporting for month over month review. Prioritize a few key charts

   1. Pipeline generated each month stacked by campaign source

   2. Pipeline closed each month stacked by campaign source

   3. Closed Lost Deals by Month stacked by Closed Lost Reason

   4. Active deals and their current status / stage

Where does CRM go long term?

Once you start using your CRM and populate it with good information, you can start do to tons of more advanced things with it that are great steps for growing your business.

Some of these may even require actual help in managing your CRM which is inevitable, leading to the photo above :)

• Customer success:

  • Track onboarding stages and health

  • Add “Renewal date”, “Plan size”, “Champion” fields on the company

  • Renewal / expansion deals with their own pipeline

• Product:

  • Log feature requests on the company / contact

  • Use “Use Case” and “Industry” to slice feedback by segment

• Finance / ops:

  • High level “bookings” view from CRM, which reconciles against Stripe / billing

  • Being able to answer “Where did this customer come from, what did we sell them, and who sold it?”

• A simple “forecast categories” concept:

  • Pipeline, Best Case, Commit, Closed Won

• A note on the power of being able to tell your board:

  • “We have $X in Commit, $Y in Best Case, and our historical win rates by stage look like Z, so here is what we expect next quarter”

Pitfalls and anti patterns

• Do not:

  • Create 200 fields because “we might use that one day”

  • Let everyone create their own fields with slightly different names

  • Have 18 pipeline stages that no one understands

  • Use free form text for things that should be picklists (industry, campaign source, plan type)

• Do:

  • Review your fields and pipeline once per quarter and delete / collapse what is unused

Below I’m going to share some fields that I like to spin up that are “Intent Signal” related but i’ll leave you with this:

Once you run your company on your CRM, everything becomes easier: forecasting, hiring, onboarding, GTM planning, raising money, and understanding what’s actually happening in your business :)

Example Fields

If you want to take a second step for signal based CRM fields, I’d recommend adding some of the below:

Company Fields

• Industry Classification - AI - Picklist

Signal Fields

• Latest Signal Name - Text

• Latest Signal Date - Date

Website Visitor

• Website Visitor - Date - Date

• Website Visitor - Name - Text

• Website Visitor - URL - URL

Job Posting

• Job Posting - Date - Date

• Job Posting - Title - Text

• Job Posting - URL - URL

New Hire

• New Hire Date - Date

• New Hire Name - Text

• New Hire Linkedin - URL

Promotion

• Promotion Date - Date

• Promotion Name - Text

• Promotion Linkedin - URL

Company Post Liker

• Linkedin Post Liker - Date

• Linkedin Post Name - Text

• Linkedin Post Link - URL

Competitor Liker

• Competitor Post Liker - Date

• Competitor Post Name - Text

• Competitor Post Link - URL

Linkedin Page Follower

• Followed Linkedin Page - Date - Date

• Followed Linkedin Page - Name - Text

• Followed Linkedin Page - Link - URL

News Signals

• News Signal - Name - Text

• News Signal - Date - Date

• News Signal - Link - URL

Marketing Fields

• Downloaded Content Name - Text

• Downloaded Content Date - Date

• Recent Event - Date

• Conference - Text

Contact Fields

• Persona - Picklist

• Latest Signal Name - Text

• Latest Signal Date - Date

• Visited Website Date - Date

• Downloaded Content Date - Date

• Latest Signal Name - Text

• Latest Signal Date - Date

• New Hire - Date

• Promotion - Date

• Conference - Text

• Company Linkedin Post Liker - Date - Date

• Company Linkedin Post Liker - Link - URL

• Competitor Post Liker - Date - Date

• Competitor Post Liker - Link - URL

• Followed Linkedin Page - Date - Date

Other

• Downloaded Content Name - Text

A few months ago, Clay announced a $100M raise; Unify announced a $40M raise about 6 weeks before.

Clay has done an incredible job building extreme depth and versatility in their product, becoming the power-platform for individual, mega-hacky GTM engineers. (But now they themselves are hiring AEs!!!)

Unify has always stood out as a far more user-friendly, scalable platform that can be used by full teams in GTM. It has dashboards, is end to end, and looks slick as heck.

It's the classic power versus convenience tradeoff, and both companies do a phenomenal job of owning their niche (and being upfront about it).

It’s going to be an all out war, and companies like Actively AI, Exa, Extruct AI, Sixtyfour, Origami and dozens more are following closely behind.

VCs are picking their horses and people are preparing for a wild battle in the modern GTM space (sorry old sequencing tools ;) ). Here’s what you should you be looking out for as a buyer, as an investor, or simply as a spectator:

• There’s companies like Clay and Unify battling for market share with their opinions over complexity versus convenience.

• There’s outdated incumbents like Apollo, Salesloft, Outreach, and more trying to stay relevant

• Data providers are also moving into the outbound game like Sumble (raising nearly $40m USD a few weeks ago?!?!)

  

• There’s at least 20 new GTM startups per year promising the world and trying to find their way

• And there’s been little to no liquidity for anyone involved

I spent a ton of time in Unify’s product while I was there, and since doing my own freelance work, have been spending 40+ hours each week in Clay since then.

It’s more competitive than ever, and yet there’s still so much to do. I wanted to briefly share my thoughts on what needs to change for a player to truly cut through the noise and build in my mind what a definitive solution is.

Signal Prioritization

I touched on this in another substack but no one has yet figured out how to intelligently prioritize signals appropriately. Imagine one person shows 3 sources of intent, how does a system of action decide which signal to action on? In Clay you can build a mega-table of 100 columns to accomplish this (but few internal teams are doing this) and it still misses the mark based on the sheer amount of data / possibilities available.

Unify structures their Plays as individual workflows for each intent signal, but the only way to prioritize signals is to set time delays to ensure you action on one before the other.

I imagine this is very difficult to do, but a large problem that someone needs to find the answer to.

Finding Someone’s “Primary Current Job”

Weirdly an annoying problem I’ve run into across GTME work is determining which role for a person is actually their “primary, current full-time job”. There are people these days that have 15 jobs on their Linkedin (myself included). Board seats, angel investors, cofounders of multiple things. You can build an agent in Clay to discern which job might be someone’s predominant role, but this is a custom AI build, and not something that is available out of the box.

For Unify, as far as I know, this is not yet a thing. With Clay you can pull this off using AI, but this should be an out of the box AI enrichment and not a custom prompt IMHO.

Ultimately data will be democratized, someone needs to build rails to pull in someone’s entire job history, but importantly, which job is their actual full-time one that they have buying power for.

Storing / Leveraging Data Across GTM

Clay is incredibly powerful because data is stored in table form. If you’re organized in setting it up, you can refer to data across table and workf

lows, store data in CRM very easily, and thus use it wherever you want.

Unify is probably building some form of list functionality, but in order to earn the love and adoration of GTM agencies and hackier GTMEs, they need to build something that is far more configurable and customizable.

Title Classification

With some exceptions, Unify does an incredible job of qualifying / disqualifying titles. They use a simple include / exclude UI/UX that when setup thoroughly, tends to do the job well. It would be interesting to use AI as a component of qualifying a person based on their title as well, but for the most part, this end of Unify’s solution is very strong.

On the other hand, in Clay you can use their formulas to create a set of inclusive / exclusive title keywords, but the UI/UX is fairly messy and difficult to audit. What’s more, the formula tends to leverage substrings rather than full words. I am surprised they haven’t built better functionality here. However, their AI offering offers a great remedy here, I just hate incurring more API usage from my OpenAI API key.

Commoditization of Email

If Clay, Unify, and the hundred other automated outbound solution companies have their way, every business on the planet can use email to do lead generation far easier than they have in the past.

As users become further drowned in sales emails that they never signed up for, which platforms can build infrastructure for other workflows? Dialing, marketing, etc.

Unify certainly will build a dialer, Clay has fairly strong marketing use cases (their Webflow API connection is dope).

These companies need to expand past solely email / outbound more generally.

Venture Will Always Back Better GTM Startups

An existential question I always asked myself while working at Unify was “won’t there always be another better Unify / Clay?”

So long as GTM tech is closely tied to pipeline generation (trick question, it always will be), operators with GTM insight will always seek to build a tool that is 3%+ better than the last. And VCs will always back them, because heck - maybe this is the one that does it!

In this space in particular, it creates a never ending cycle of rip and replace.

If the 2010s marked the success of Apollo / Outreach / Salesloft and the mid 2020s marked the entrants of Clay / Unify being the hot new GTM companies, it wouldn’t be surprising if we saw another set of tools promising an improvement over Clay / Unify in the next 18-36 months.

We’re already seeing a massive influx of YC backed companies and other startups like Floqer, Sixtyfour.ai, Origami, Bitscale, and more.

Will this cycle end? I think not, but the ones that will survive will be extremely opinionated on what they want to build, and more importantly, for whom.

1. Jack of Trade (master of none) - So many of these platforms ultimately land up building everything. Their UIs go from elegant to bloated and drowned with features (Apollos sidebar is the most crowded thing I have ever seen, I sweat every time I login). Choose what to be a master at and don’t enter into spaces you can’t win in

   1. This means companies pick a handful of features and crush them, and don’t deliver things outside

2. Brand - those that capture mindshare and build sustainable and relatable brands will take the cake. Unify and Clay have done an exceptional job with this.

   1. Unify anchors the brand in futuristic workflows, modernity, and simplification

   2. Clay anchors the brand in fun / creativity, but also depth and complexity

3. Future-Proof - don’t build a company that just sends emails if we believe email is dying. Diversify your bets and be sure to enable other sources of pipe-gen to be a big part of your product.

Ease of Use

While everyone has one (an email), the science behind email is actually quite complex. And to be frank, few people really have a deep understanding of how email deliverability actually works. Unify and Clay both employ enormously large success teams, external partner agencies, and more - it takes months to see success and has created a massive headache for companies in onboarding and ongoing success.

Clay is an incredibly complex product which I love for that reason, but for most people it is far too complex (even with Sculptor). Unify is far simpler, but sales folk still don’t want to build 800+ different plays and constantly manage their dependencies.

Switching Costs

Everyone cringes at the thought of switching systems. And with how deep tools like Clay go, I can not fathom needing to rebuild my workflows in Clay in another tool. For how painful Clay is, I know that I can go extremely deep and be extremely precise, and evaluating other tools is something I enjoy recreationally, but I genuinely wonder what it would take for me to truly migrate my workflows from one to another.

Moreover, I continue to see GTMEs fight over the classic bundling/unbundling conundrum. Will tools win that are able to do “everything”? Or should tools stay opinionated on one to two areas that they know that they can perfect those workflows.

Evolution of Business Development

Do we collectively believe BDRs will cease to exist?

Will there be a small set of elite BDRs that work in conjunction with GTMEs?

Every company has an opinion, and while we will most certainly land up in the middle, I tend to believe there will be more automation and less typical BDRs in the future. If i can send 1,000,000 emails in a month as one persona and optimize my stack and funnel to achieve a 2% positive reply rate - that’s 20,000 positive responses. Of course there are costs of such a system, but the bar we are setting for BDRs to compete is unrealistic to expect of them!

What About Non-B2B SaaS?

The most interesting takeaway I’ve had working in this space is actually how valuable lead-gen and GTME work is to non traditional B2B SaaS spaces. I’ve been doing work for some niche PE firms, construction businesses, physical guarding / security businesses. There is such a severe gap in technology for these companies, and they undoubtedly have the most to gain.

Clay’s RSS feed is a testament to this, it is their least marketed part of their product, but the area I have seen by far the most initial success from.

I can ingest any RSS feed (so pretty much any news article on the planet) and use it to conduct outbound? That’s relevant to nearly every business on earth. If someone on Clay’s CS / Marketing team is reading this, reach out (unless Brett Fischler ships something first :))!

Mailbox Rotation

Referencing back to the email being a black box, the fact that email tools (Instantly, Apollo) and Unify / Clay have not figured out domain rotation / optimization yet despite citing it is insane. We should be building these companies to generate pipeline with less emails, not more, and its incredible that I need to build custom scripts in Google Appscript or Cursor to interact with Smartleads / Instantlys API rather than these products having solutions out of the box.

Dashboarding & Reporting

Being able to concretely show ROI for business users is just as important as actually delivering that ROI. Unify’s dashboarding is quite beautiful but historically has loaded slower. Clay certainly will drop something in this category, hopefully its lightweight and feels easy to use.

Other Niche GTM Edge Cases

• We need these outbound platforms to routinely check for updated contact info on a recurring basis irrespective of the “intent source”, as these leads die immediately (in an outbound sense) if contact info isnt found / is stale

• Being able to copy paste workflows / nodes / columns across workflows. A lot of workflows have the same plumbing, Zapier does a great job at this

The GTM workflow for teams of all sizes / spaces is changing fast, it will be exciting to see how this all plays out

In this substack

Why signal prioritization matters in scaled outbound – The mistakes I made at Unify sending 1.4M+ emails – How we fixed issues with reply rates, fatigue, and targeting – The future of outbound systems

Scaling Automated Outbound as a Channel

Running Automated Outbound is a lot like going to the gym - you see initial traction relatively quickly and then you need to hunker down and begin to get methodical to see incremental progress and momentum. In the 13 months at Unify, my work had sent north of 1.4 million automated emails via the Unify platform. Without a comprehensive tool like Unify (or other solutions), a feat like this is rarely possible.

The pipeline the growth team and I generated while at Unify, using Unify was nothing short of spectacular. In my 13 months at Unify, we generated north of $12 million dollars in pipeline. This pipeline came from a substantial number of intent signals and plays that we built using Unify, integrated with other tools, and more - which gave us a diverse set of sources through which we booked meetings from. Every day, we ran 40+ plays, which funneled into 100+ sequences and the list continues to grow.

To name just a few of our plays:

• Website visits (normal website visits, UTM / paid traffic retargeting, individual intent, content downloads, etc)

• Linkedin engagement (followers, likers, commenters, profile viewers)

• Email engagement (link clicks, replies, sentiment, etc.)

• G2 intent

• New hires & champion tracking, Promotions

• AI agent qualification with infinity signal for recurring account research for

  • CRM-based plays (Closed Lost Revisit, Follow ups, etc.)

  • Fundraised

  • Website Tags

• RSS feeds (the hidden gem of outbound IMHO)

• and much, much more

We’ll walk through 2 chapters

1. The roadblocks I hit as we scaled automated outbound

2. What is intent signal prioritization and how to account for it

Unify’s Automated Outbound Roadblocks

Hitting the breaking point

Over the first 9 months as we scaled our automated outbound program, we rarely ran into problems - it was common for us to easily accommodate our email backlog from the email send capacity we had. Additionally, each week we saw a diverse selection of meetings being generated from outbound plays, with little / no need to focus on optimizing for any particular one.

After all, if it ain’t broke - dont fix it, right?

Unfortunately, before we knew it, we had fallen into a trap of our own doing. As the program continued to grow and we became more bullish on email, we actually ran into a few fascinating problems. Notably:

How do you know when to prioritize a signal at the person level or a signal at the company level? Moreover, all things being equal, how does your outbound system prioritize what to write in an email if more than one signal is present for that person?

These things tend not to affect smaller automated outbound motions - but for us reaching this point was inevitable. The learnings I share below will hopefully help you build your framework for prioritizing signals and avoid the same mistakes I made!

When (and how) I realized I screwed up

On Monday February 10 (coincidentally my birthday), we had our best day ever at Unify. Just Unify’s automated outbound program alone booked a large number of high quality meetings with top prospects at target companies. As we continued to ship new signals and product improvements, it was impossible to imagine we would hit a huge roadblock. Due to the success of the program, I began to take a number of risks all at once without trying each one incrementally. A few of the things that I did were:

1. I removed booking links from our automated email signatures in the hopes of improving deliverability even further.

2. I A/B tested across website views sequences by having no links in any email outside of the opt-out link

3. With our public launch of AI agents and the infinity signal, I moved many of our recurring scrapes that occurred outside of Unify, directly onto the Unify platform - including signals like

   1. Recent or Future Event mentions on website

   2. Hiring for certain jobs via job listings

   3. Recent or Future Webinar

   4. Recent of Future Earning Announcement

   5. In the same light with our launch of email classifications and enrollment results in Audience builder, I began to build sequences for new permutations of email enrollment results

What were the consequences of our massive outbound program?

1. The sheer number of new agent scraping we had running caused us to generate a lengthy email backlog

2. The large number of signals we were monitoring on every company meant we were running out of people to email (lol)

3. I (at the time) was not monitoring reply rates by mailbox to determine which domains and mailboxes were showing signs of fatigue and poorer deliverability

4. I didn’t prioritize dashboards to manage results and progress in real time

5. Not prioritizing person level signals versus Company level signals

1. Removing Booking Links in the Email Signature

Lesson: While it is widely considered problematic to include links in emails (especially if they differ from the domain you are sending from), providing a booking link actually makes it far more convenient for prospects to book time with you. If you don’t prospects will have to respond to an email, work thru back and forth of scheduling, and more. This significantly reduces the barrier to prospects learning more about your solution.

While no show rates may be higher and conversion on a opportunities without an actual email thread may be lower, this resulted in a drop in meeting generation.

Learning: I added back signatures in our emails to lower the barrier to booking a meeting for prospects.

2. A/B Testing for Other Links in Emails

Lesson: Similar to the above, removing links to unifygtm.com from our emails meant that prospects could not easily access the website and do their own research and discovery. While keeping emails light on links / HTML is key for deliverability, I believe the sacrifice in deliverability is occasionally worth it so that prospects can easily learn more about the product you might be pitching.

3. AI Agents for Every (Lower Converting) Company Signal

Lesson: Launching AI agents was a huge milestone for Unify - giving our customers the ability to qualify or disqualify any number of companies (at scale) based on criteria they set that is both dynamic and intelligent. However, what I failed to realize initially is that just because I could now do this, that didn’t mean that I should. Of course the team wanted to prioritize more signals, more emails, more meetings, more pipeline, but we didn’t think through the process of what could occur.

Within days we had enrolled thousands of peoples into outbound sequences that were company-level signals, and our composition of enrollments moved from predominantly person-led signals to company-led signals, which are understandably lower-converting.

This makes sense because if we discover something about an individual person, this is likely a better signal than a signal in relation to the company they work for. This over enrollment of people into lower converting signals had a number of downstream consequences

1. The people we emailed could not be emailed for other intent signals that may be of higher quality because they were in this sequence already

2. The people we emailed could not be enrolled into other sequences until their cooldown was completed. On Unify you can set your own cooldown period (a timeframe under which someone could not be emailed again) - we recommend 30 days

3. Lower quality signals result in higher opt-out rates or likelihood to opt-out, meaning you can’t email them again.

Additionally, based on high level cuts of data, person-level signals tend to have much higher reply rates and booked meeting rates when compared to company-level signals. Empirically, we have observed as much as a 2X+ improvement in reply rates for person level signals over company level ones.

Interestingly, we also track peoples clicks and opens in old emails (what we call email engagement). Since we had enabled users to filter audiences based on how people engaged with emails, I decided to email those who had never previously responded to an email with a highly customized email to get them to re-engage. While this felt like a smart idea initially, it is generally not a good idea to do this as they are unlikely to engage if they haven’t engaged already!

Learning: We quickly realized we needed to reevaluate which company-level signals were worth automating and which to hand to BDRs.

4. Creating a Lengthy Backlog from Too Many Signals

Lesson: Additionally with the release of AI agents, we now had the ability to monitor for a near infinite number of signals. This meant that we began enrolling thousands of people into sequences, creating a tremendous backlog of emails to be sent. At Unify, we recommended each mailbox sends no more than 25 emails per day. If we were to generate 200 emails for one mailbox in one day, this would take roughly 8 business days to work through the backlog assuming no new enrollments are made and excluding the affect of more messages associated with those enrollments (email 2, 3, etc.)! This is extremely problematic, as certain signals need to be prioritized sooner - you don’t want a signal to go stale! The last thing you want is an email for someone visiting your pricing page to go out several days after an email for someone who was recently hired at a new company!

Learning: We moved to create mailbox groupings based on the level of the intent, and dynamically calculated the sending volumes we would need for each one:

1. Instant Send - a mailbox grouping used for the highest intent signals like people viewing pricing pages or product demos, people who fail to book meetings, and more

2. High Intent - people who view other high intent website pages, view or engage with Linkedin content, etc.

3. Medium Intent - people who engage with us or competitors on G2 or on Linkedin, regular website views, and others.

4. Low Intent - used for cold or mostly cold emails

5. Running Out of People to Email

Lesson: If you try to email everyone, you will. And then there will be no one left to email! This comes back to some of the earlier points, but because of the sheer number of signals we were emailing for, we began to return zero people from prospecting because we either couldn’t find additional people at a company or the people we did find were in other sequences.

Learning: Again, this forced us to re-evaluate which company level intent signals are most important to us, which ones we should outbound based off of, and how many people to email for each one!

5. Not Monitoring Mailbox and Domain Reply Rates

Lesson: In any large scale outbound motion, it is critical to monitor reply rates as a heuristic for performance and thus deliverability. At the time, we were using one massive round-robin to enroll people into sequences. We did not have any focus on the reply rates of individual mailboxes, domains, and more. This type of setup causes the poor deliverability of mailboxes to compound and continue to worsen.

Learning: We moved to monitor the reply rates of every mailbox being used on a regular 2 week cadence. This had allowed us to reactively pause sending for lower performing mailboxes and allow them to recover, and proactively move mailboxes that are trending in the wrong direction into warming or a pause state to allow them to improve.

6. Metrics, Dashboards & Monitoring

Lesson: When I joined the startup world, I found it hilarious how sales people hated updating and using CRM / Salesforce. They told me it’s so much easier to function based off gut and intuition. My version of not updating Salesforce was creating and monitoring dashboards day-in and day-out. On a small scale, its easy to act based on intuition or as I like to say a finger in the wind, but inevitably, things quickly begin to slip thru the cracks. Every team needs a comprehensive way to monitor successes, but most importantly failures.

Learning: Throughout March and April of this year, I spent a considerable amount of time writing SQL queries to monitor all of our outbound metrics in close to real time and ensure that we had an active pulse on what was going on. Much of this effort was what led me to build Galaxy.

Intent Signal Prioritization

What is intent signal prioritization?

Intent signal prioritization is the ability to dynamically stack rank signals based on the likelihood that outbounding based on that signal will convert into a meeting, a qualified opportunity, and hopefully a closed-won deal. In the early days of outbound (and because email is such a low CAC channel), prioritization is typically not a top priority for GTM engineers - early outbound automation is focused on a) identifying the relevant signals and b) building process for outbounding based on these signals consistently. As the scale of your outbound systems grow however; you will inevitably encounter this problem. This happens for a few reasons:

1. Sending Constraints

   1. You have a constant number of mailboxes with a limited number of email sends per day

   2. You are running an increasing number of outbound experiments which reduces your available send capacity

   3. You must be careful how many emails you send and which emails you send so as to not hurt email deliverability

   4. Generating a high backlog means intent signals will go stale

2. Company Constraints

   1. There are a limited number of people you can email at any given company

3. Intent Signal Constraints

   1. Intent signals tend to occur at totally different times

   2. The conversion rate from any two signals may differ substantially (someone taking a meeting that originated from an offer for a Gift Card will likely convert substantially lower than a meeting that is booked from a Competitor teardown campaign).

Before you know it, you went from being able to email freely with no constraints on how or why you outbound, to needing to optimize for the the best signal. Without a focus on prioritization, you may actually email people for lower converting signals, and this caused us to re-evaluate the framework through which we conduct our outbound motion, with strategy, timing, and more at its core. As detailed above, we faced this problem firsthand in March of 2025!

The importance of signal prioritization

In the world of outbound at scale, intent signal prioritization is the name of the game. To start small, let’s pretend we are the GTM engineer for Salt.com and Alan (who works at Pepperman.com 🫑) is a prospect that we want to book a meeting with. With Unify, we can surface a large number of intent signals from Alan at Pepperman.com. Some of these signals might be:

• Has Alan visited our website

• Has Alan visited high intent pages on our website (pricing page, sign up for a demo, our integration docs)

• Has Alan downloaded a piece of content

• Has Alan checked out our product walkthrough

• Is Peperman.com hiring for GTM engineers

• Has Alan or Pepperman.com recently hired a GTM engineer

• Are target personas at Pepperman engaging with my Linkedin page by liking, commenting, or viewing my profile

• Has Pepperman.com been mentioned in the news

Without an automated outbound solution, GTM teams with this number of signals typically resort to one of a few strategies, all of which are not optimal. This may be in the form of BDRs just outbounding based off of what they see first, drip campaigns for all of them, and more. Typically none of these are the right solution…

Company vs Person Level Signals

As we dug deep into the data, we had a few interesting findings, that our composition of sequence enrollments shifted from predominantly person-level intent based to company-level intent based. What’s more is that we found historically that company-level intent based sequences showed roughly 2X worse reply rates than person-level intent based sequences. These were two fascinating realizations that informed how we reworked our process for running outbound and how to optimize it.

How do other platforms prioritize signals?

There’s a wide spectrum of outbound tools on the market. In relation to signal prioritization, I wanted to take a moment to delineate what each one of them does, and how we differentiate from them

1. Email Tools

   1. In email tools, there simply is no signal prioritization, meaning you have to manually choose between one signal or another in all cases. This is fairly common for tools like Apollo, Outreach, Salesloft, and many more. You can drop someone into a cold email sequence for this, or for that.

2. Warm Outbound

   1. In warm outbound tools, the platforms typically do not even have enough intent signals to actually justify any type of signal prioritization or framework. They tend to just involve website intent, and so there is no need to do any type of prioritization

3. Intent Signal Aggregators

   1. intent tools provide you tons of intent signals but no comprehensive way to score / optimize for which ones to email off of, especially since the outbound (and thus conversion) does not live in the tool.

4. Clay and other Lead Workflow Tools

   1. While they don’t want to admit it, the majority of people using Clay are actually using it for cold outbound with account research that makes that outreach slightly warmer. With a few exceptions, prospects that are emailed from Clay haven’t necessarily shown interest in your product so there isn’t a massive prioritization component that is critical to success.

   2. However, for the minority of people, in order to achieve some form of signal prioritization, you need to build one table that has dozens of signals integrated to it across hundreds of columns. To do this requires building a comprehensive scoring system for each signal, and a waterfall of signals to determine which signal will be the one that is emailed about. This is extremely difficult to build (and maintain) and even more difficult for most people to wrap their heads around. More importantly, a complex Clay table like this creates a ton of tech debt and key person risk associated with whoever built it.

Additional Intent Signal Optimization Problems

There are a handful of criteria that make this an objectively difficult set of problems to solve. Lets look at few of the ones that were not covered in the Unify Roadblocks section above, with a few examples. Let’s again pretend that we are the GTM engineer for Salt.com and Alan (from Pepperman.com) is a prospect that we want to email.

Consecutive actions

In the case of Alan interacting with Salt.com (our hypothetical website), there are a number of user flows that might occur

• Alan simply visits the website and then leaves

• and then Alan downloads a piece of content or an ebook (like this one)!

• and then Alan visits the product walkthrough (we use Navattic for this)

• and then Alan tries to book a meeting but doesn’t (we use Default for this)

• and then Alan successfully books a meeting and an opportunity is created in CRM.

Each one of these outcomes alone, requires its own unique email campaign.

But, if they all happen one after the other (which is fairly common!), we need to wait to make sure to choose the highest intent signal to email for!

This could look like:

1. If someone just visits our website we wait to see if they did anything else

2. If someone downloads a piece of content we wait to see if they did anything else

3. If someone tries to book a meeting but doesn’t we wait to see if they were able to book a meeting

4. If someone books a meeting and an opportunity is generated, we shouldnt email them (asking them to)!

This type of logic is not present in other outbounding tools, but with Unify’s Play builder, you can easily use logic workflows and time delays to account for the actions that are relevant to you and how to prioritize them. This means that using Unify you can make sure that consecutive actions like these are accounted for and that this prospect will be emailed for the greatest intent signal that is displayed by Mr Alan from Pepperman.com

Timing

Signals never present themselves all at the same time. Let me show you what I mean. Alan from Pepper.com is not capable of doing all of these things at the same time:

1. viewing our website

2. viewing our product demo

3. tries to book a meeting

4. likes my Linkedin post

5. moves jobs

Rather, these things tend to happen one after the other, in no particular order:

1. Alan from Salt.com views the Pepperman.com website on Monday at 11AM.

2. Alan views the product demo at 2PM on Tuesday Play Prioritization - EBook 16

3. Alan tries to book a meeting but doesn’t on Tuesday at 6PM.

4. Alan likes my LinkedIn post at 6PM on Friday.

If you took a snapshot of Alan’s activity at a certain point in time, say Tuesday at 1:30PM, all you would see is that he viewed our website, even though he is going to view the product demo in <30 minutes! In an optimal scenario, would it make sense to email him the second that intent is shown, or wait to see if other intent appears?

The future of intent signal prioritization

The future of automated outbound systems need to account for this type of chronological but disparate sources of intent to ensure you are emailing someone for the best signal across that time frame, and take into account any type of predicted behavior that may occur in the near term!

This is why I firmly believe in investing in your website, brand, and creating community early → I’m a huge fan of the team at Default and their product’s ability to build infrastructure for inbound pipeline gen for this reason.

I lead our focus on inbound from the very start at Galaxy, by putting tons of time into our website, holding weekly events, even promoting jobs for our users, etc.

My time at Unify was spent mostly focusing on outbound automation. Intent signal scraping (using tools like Phantombuster and Captain Data), orchestration (using Unify and Clay), the outbound itself (using Unify’s sequencing), deliverability and reporting (using homegrown data analytics dashboards in Redash, part of the difficulty of doing so which was the inspiration for Galaxy), and other sales enablement / revops work.

I had no idea how to effect change on the Inbound channel.

When I started working on Galaxy, it was really up to me alone to identify areas we could generate demand and interest and then go and act on it.

For devtools, there are only a few buckets of demand generation that we could easily spend time working on while we bootstrapped. Product-led growth, community building, and finally search-engine optimization (SEO).

I did tons of reading over the course of a month to learn more about SEO, AEO, and more. And even after my departure (cofounder breakup), Galaxy is mere hours away from hitting 10M impressions and 65K+ clicks in the last 8 months.

This type of exposure has resulted in thousands of signups / downloads per month from my work. I’m going to share my breakdown of the best practices below so that you can emulate them from a standalone GTM engineer and operator.

SEO Principles

When most people think of SEO, they still think about gaming Google - keyword stuffing, backlinks, meta tags. But that’s not the game anymore. Modern SEO is about clarity: telling both humans and machines what your product is, why it exists, and who it’s for - and doing it in a way that compounds over time.

It starts with language. Use the words your users actually type, not the ones you wish they did. At Galaxy, we originally called the product a “query client.” No one searched for that. People searched for “SQL editor,” “data workspace,” and “PostgreSQL client.” That small realization completely reframed how we positioned ourselves and how users found us.

From there, map your keywords systematically - by intent (informational → transactional), length (short-tail vs. long-tail), and type (branded, non-branded, seed, secondary, etc.). A good SEO strategy doesn’t just chase volume; it builds a web of content that catches users wherever they are in the journey. The blog post that educates today becomes the conversion page that sells tomorrow.

I used Ahrefs religiously - to find keywords, check their difficulty, and plan what content would actually move the needle. Once you know what to target, start thinking about how to bring that content to life. For most companies, this looks like a mix of persona-specific use cases, product tours, free tools, learning hubs, job boards, and comparison pages - anything that provides real value and direction for your audience.

Then comes structure. Every page should be clean, fast, and easy to navigate. Use clear heading hierarchy (H1s, H2s, H3s), descriptive URLs (/learn/data-tools not /posts?id=12345), internal links with contextual anchors (“SQL Editor Comparison,” not “click here”), and outbound links to credible sources like dbt or Snowflake.

Under the hood, structure your data with schema - JSON-LD for your Organization, Articles, FAQs, Authors, Jobs, Reviews, and Software Products. Schema helps Google and Bing understand what your site means, not just what it says. I also recommend adding a robots.txt and even an LLM.txt to guide both crawlers and large language models - because the next generation of traffic is already coming from AI systems reading the open web.

One thing I learned the hard way: you can build authority on your site, but the real lift happens when others validate you. When Galaxy started getting mentioned in newsletters, forums, and job boards as “that modern SQL editor,” everything changed. Those external signals - backlinks, brand mentions, co-citations - are the off-page engine of SEO. Build relationships, guest post, and track your referring domains. When a high-reputation site links to you, you borrow some of its authority - what SEOs call “link juice.” (lol). That’s the compounding power of credibility.

Structure isn’t just for machines - it’s for humans. The best-performing content we published at Galaxy always had a Table of Contents, clear sections, and optimized meta tags (titles, descriptions) that matched search intent. Even small details matter: filenames like sql-editor-workspace.jpg with alt text like “modern SQL editor desktop workspace” outperform generic IMG1234.jpg. Every image, video, or asset should have a descriptive filename and alt tag - it helps both accessibility and indexing.

Content quality still rules. Google’s E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) is worth paying attention to. Show who wrote each piece, include author bios and credentials, and -when possible - feature real quotes or testimonials from users. This turns marketing copy into proof of expertise.

Don’t overlook technical experience either. With mobile-first indexing, your site needs to load fast and read cleanly on phones. Fonts should be legible, buttons tappable, menus visible, and layouts responsive. Run regular audits with Google Search Console or PageSpeed Insights and fix anything slowing you down.

Finally, break up dense text. Long paragraphs kill engagement. Use bullets, tables, and call-outs to make your content easy to scan. Clear formatting improves dwell time, readability, and crawlability - all ranking signals that add up.

And the golden rule: don’t let your content go stale. Refresh it. Track backlinks. Resubmit your sitemap. SEO is not a one-time setup - it’s a compounding system. Every new page, keyword, and external mention adds long-term equity to your brand’s discoverability. Think of it like interest - slow at first, exponential over time.

AEO Principles

Answer Engine Optimization (AEO) is the next frontier of inbound. SEO helps you show up on Google - AEO helps you show up everywhere else: ChatGPT, Perplexity, Anthropic, and whatever comes next.

AEO is about giving machines short, high-fidelity answers to human questions. You’re writing for the summary layer of the internet, not for clicks. The goal isn’t to sell - it’s to be the single, confident answer when someone asks, “What’s the best SQL editor?” or “What are the top DataGrip alternatives?”

The playbook looks different from SEO. Lead with clarity and structure: write in question-and-answer formats, start with direct definitions, and use schema markup like FAQ, Q&A, and Speakable to help models recognize your content as authoritative. Back it up with author bios, references, and testimonials - these act as trust signals for both humans and algorithms.

Freshness matters more here than anywhere else. LLMs and modern search engines reward recency, so revisit your best content quarterly - update examples, adjust timestamps, and add new context. If you’re in a fast-moving category like AI or data, this isn’t optional.

Next is entity linking. Use sameAs attributes across the web (LinkedIn, Crunchbase, GitHub, your website) so your company and authors form a verifiable footprint in the knowledge graph. The stronger your entity identity, the more confidently AI systems cite you.

And don’t just publish - participate. Real discovery now happens in discourse: Reddit threads, G2 reviews, Stack Overflow answers, niche Slack and Discord groups. When people mention your brand in natural conversation, AI models notice. That’s modern distribution.

At Galaxy, I made it a rule that every post had a byline, author bio, and (when possible) a real quote from a user. It turned generic blog posts into credible proof points. Search engines read that as E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). Readers just read it as real.

You can game some of this, and you should, but don’t overdo it. The best inbound strategies work because they’re consistent, authentic, and high-signal. If you’re using AI-generated content, tuck it deep in your site - not front and center. Preserve your brand integrity, and let your genuine expertise do the talking.

If SEO builds discoverability, AEO builds authority. Together, they’re the twin engines of modern inbound - and your most compounding assets in a world run by algorithms.

There are a handful of tools that I mentioned above, but in the spirit of inbound automation, I wanted to toss out a few others that I have used personally and highly recommend:

• Default - Inbound automation, scheduling, and orchestration

• Gauge, Profound - Answer engine optimization

• RB2B, Vector

• Clay / Unify for inbound-led outbound

• ahrefs - for SEO discovery and keyword analysis, backlinking etc.

• Google Search Console - sucks but necessary for tracking

• Marketing attribution - Amplitude

• Website builder / hosting - Webflow

• A/B Testing of marketing websites - Keak

Closing Thoughts

There’s no silver bullet for inbound. Anyone selling you “instant SEO” is lying. The work compounds slowly, like investing. Every blog post, keyword cluster, and community mention is a brick in your distribution moat.

SEO builds discoverability. AEO builds authority. Together, they make your brand findable, credible, and defensible.

If I had to start over again tomorrow (which is what i’m doing lol) - new product, new domain, zero budget - I’d still start here: a strong website, structured content, clean schema, and a reason for people to talk about you.

That’s the foundation of inbound growth. Everything else is just noise.

Some Bullet Point Notes from Me 🙂

• Map keywords in relation to what value they will provide to your website

  • By search intent

    • Informational: Users are looking for information, such as “how to tie a tie” or “what is SEO”.

    • Navigational: Users are trying to find a specific website or page, like “Facebook login” or “Amazon homepage”.

    • Commercial: Users are in the research phase, comparing products or services, for example, “best running shoes 2025” or “[product] reviews”.

    • Transactional: Users are ready to make a purchase or take a specific action, such as “buy iPhone 15” or “[service] sign up”.

    • Local: Users are searching for a business or service in a specific geographic area, like “coffee shop near me” or “[city] plumber”.

  • By length

    • Short-tail keywords: These are broad, generic terms with high search volume, like “shoes”. They are often difficult to rank for.

    • Long-tail keywords: These are longer, more specific phrases that have lower search volume individually but are easier to rank for, such as “waterproof trail running shoes for women”.

  • By other strategic factors

    • Branded: Keywords that include a specific brand name, such as “Nike running shoes”.

    • Non-branded: Keywords that do not include a brand name, such as “best running shoes”.

    • LSI (Latent Semantic Indexing): Keywords that are semantically related to a main topic, which helps search engines understand the context of your content.

    • Seed keywords: The basic, core terms that you build keyword research from.

    • Primary keywords: The main keyword a specific page is targeting.

    • Secondary keywords: Supporting keywords that are related to the primary keyword and add more context to a page

• Populate JSON LDs for

  • Organization

  • FAQs

  • Article

  • Author

  • BreadCrumb List

  • Event

  • Job Posting

  • Profile Page

  • Q&A

  • Review Snippet

  • Software App

  • Speakable

• Table of Contents

  • Meta Tag and Meta Description

  • Image Tags

• Internal Linking wherever possible with descriptive anchor text (“SQL Editor Comparison”) - not “click here”.

• Mobile breakpoint optimization

• Create keyword pillars

• Highly structured

  • bullets, tables, etc.

Notes

• Determine the words used to define / describe what your product is / does

• Determine what words your competitors use to define / describe their product

• Use a tool like ahrefs to do more keyword discovery and determine what people are actually searching for (Search Volume) and the difficulty of ranking for those keywords (Keyword Difficulty), sometimes what you think might be used to define your product is different from what people actually search for! For example we described Galaxy as a query client and then

• Be thoughtful about content you can create on your website to offer value / direction for your users but also to inform about your product.

  • For this reason, many companies have categories of pages like persona specificUse Cases, product tours, free-tools, job-boards, learn pages, competitor comparisons, and more

• Add images with descriptive file names and keyword rich alt text

• Use of proper headings and hierarchy (H1s, H2s, H3s, etc.)

• Logical navigation

• Fast site speed

• Table of Contents

  • Meta Tag and Meta Description

  • Image Tags

• Internal Linking wherever possible with descriptive anchor text (“SQL Editor Comparison”) - not “click here”.

• Outbound Links to authoritative external sources (e.g., dbt docs, Snowflake blog).

• Make URLs descriptive and clear

  • /learn/data-tools ✅ not /posts?id=12345 ❌

• Mobile breakpoint optimization

• Create keyword pillars

• Highly structured

  • bullets, tables, etc.

• Backlink and track backlinks

• Submit sitemap to Google Search Console and Bing consistently to push updates

• Create a robots.txt

• Create an LLM.txt

AEO Principles

• Try to create short punchier information with answer snippets at the top

• Prioritize question and answer format for answer engines

• Try to get mentioned in areas of genuine discourse / discussion like Reddit, review sites (G2), etc.

• Include authors and testimonials in articles to show reputation / official nature of what has been written

• Content recency / refreshes

• Create content in relation to specific searchers users might have

  • What is the best SQL editor

  • What are the best alternatives to DataGrip

• Use sameAs Attributes for Entity Linking

• Optimize for mobile

I’m also starting to work on something new. If you know any cracked technical builders who’d be excited to cofound with someone who handles all things GTM, growth, and ops - send them my way :)

Signals → Enrichment → Orchestration → Outbound → Reporting

The goal of an automated outbound channel is to successfully connect tools together in such a way that you bridge signals all the way through outbound.

But, automated outbound is just a small piece of GTM engineering

From consulting projects i’ve worked on, to my own personal experience at Galaxy, to that of my experience running automated outbound at Unify, automated outbound is one of the cheapest and largest buckets of pipeline opportunity that founders and GTM teams have at their disposal.

Automated outbound is so powerful because it is extremely scalable with relatively minimal cost. While it takes time and effort to spin up; email, when done right - can be an incredible lever to help you in the founder discovery phase, through product market fit, to growth and scaling.

Many teams try to implement outbound automation at various points of their company’s lifecycle, and while some are successful; many fail. There is a plethora of reasons for such an outcome, but more importantly - an unsuccessful outbound automation program does not mean the principles behind the initiative or the tools involved, can’t help you in other ways.

So before you set out on your optimistically nosedive into sending a billion emails and get a 50% reply rate, remember to map out what else might be useful for your GTM team, your customer success team, or more broadly for the business. These tools have been built to be helpful in many ways, and not all of them are outbound related!

Common Mistakes I’ve Seen / Why Outbound Doesn’t Work

Before we get started, I want to share some reasons i’ve seen where outbound might not work and why!

1. You haven’t yet determined if your buyers respond to emails / your space is relationship based selling

2. People screwing up DMARC / SPF / DKIM setup

3. People sending from their primary / core domain

4. You have not yet nailed down ICP and are trying to email too many personas

5. Messaging isn’t tested or validated

6. There isn’t someone (ideally internally) spending a few hours a day / week, or working full time on outbound - it needs to be an actual strategy, not an after thought

7. You’re only emailing a small number of people based on signals and then your emailing everyone else without prioritizing, you need to score your TAM ideally!

8. Too early in the company life cycle - Do you have a product to show, especially a self serve one or a self-serve demo?

9. No feedback loop with sales / CS - If sales doesn’t validate which signals actually led to revenue, marketing/ops keeps feeding in garbage. Outbound needs cross-team alignment, not just automation in a silo.

Getting Started

The way I kickoff any consulting project - involves building an intimate understanding of a few things

1. What GTM systems does the company have in place?

2. What is your ICP and how do you qualify them?

3. What signals are relevant to your business?

4. Who do you compete with and how do you differentiate?

Existing Systems

For many companies i’ve spoken to, they’re still using old sales tools and playbooks, have outdated CRMs, no direct booking forms, and lack a modern GTM stack. This part of my discovery simply aims to investigate whether I think there are areas you can modernize your stack.

Qualification (& Orchestration)

Assuming we can identify an account that has shown intent, the next step is deciding if that company is actually worth pursuing… Every email you send costs money (and can hurt deliverability) and so it is important to be clear as to who is a good fit, and who is not.

For example, things like:

1. Is this prospect an existing customer

2. Is this prospect in an active opportunity?

3. Is this prospect a competitor, a VC?

4. Is this prospect in a geography or end-market we can serve successfully (due to language constraints or otherwise)

If you can make it past some simple disqualification questions, the big outstanding question is, on a scale of 1 to 10, how good of a fit is my product for this prospect. I like to use scoring to do this (at complex levels of GTM automation) but at simpler levels, a yes, no maybe framework is a fine way to start. It’s important to not overcomplicate at the beginning :). For a company like Unify, this meant asking questions like:

• Does this company have a website (that interested customers will and do visit)?

  • Does this company have a large number of monthly website visitors?

• Is this company B2B?

• Does this company’s buyers live online or have work emails?

• Does this company’s buyers have an existing outbound tool?

You can imagine a world in which we use a scoring methodology to create one overarching ICP score, and then use that as a metric of which accounts to prioritize and send emails to, and which not to.

What i typically do is decide on a list of firmographic and technographic metrics or criteria that I want to score, assign each criteria a maximum value, and then score and sum all of the individual scores to come up with one overall score. This allows me to map a list of accounts by this score, and prioritize accordingly.

Tools like Clay, Unify, Pocus, and hundreds of others help you qualify, enrich, and route leads to all of your GTM tools (email, Linkedin, dialers, etc.) so that you can action on intent, all without lifting a finger :)

On the actual sequencing end, I am a huge fan of Instantly for emailing (I have a tough time with Smartleads UI/UX), I like Dripify and HeyReach for Linkedin messaging, and am obsessed with Phantombuster for Linkedin data automation. I haven’t used dialers but know everyone uses Nooks or Orum, and know TitanX is a new and hot dialing optimization product.

Signals

Investigating signals means having an honest conversation with your sales teams and asking - what are the reasons that make it a good time or reason for me to reach out to someone? I’ve shared a handful of my favorites below, but its important to be creative and really make this a exploration exercise.

Importantly, do yourself the favor of stack ranking signals by importance / relevance, and clearly outlining which will resonate more than others. Just because someone raised a round of funding doesn’t mean someone is the perfect prospect for you - it just means they have money to spend ;)

We saw this first hand at Unify, signals that we were able to detect at the company-level (hiring for a SDR, recently raised some funding, mentioned in the news) all converted at a very low rate. Person-level signals converted nearly twice as frequently as those identified at the company-level.

It’s crucial to know where to spend your time, and where not-to!

This is a non exhaustive list of signals I’ve used (and not just a never ending list of signals you won’t know are useful)

Signal Breakdown (C = Company-level, P = Person-level), sorted most warm to least (in my opinion)

• Cold

  • Lookalike - C

  • Bespoke Signals - C

    • Exa / Extruct

  • News / RSS Feeds - C

    • It’s crazy Clay doesnt market this signal more, its an absolute gamechanger

  • New Job / Promotions - P

  • Fundraised - C

  • Job Listings - C

  • Financial Reports - C

  • Made a List - C

  • Alums - P

  • Job Anniversary - P

  • Gift Card - C

  • Tech Stack - C

• Warm

  • Meeting not Booked / Dropoff - P

  • Product Tour (Navattic, Supademo, Olto) - P

  • Content

    • Content Downloads - P

    • Free tool usage - P

  • Website Visitors - C / P

  • Champion Tracking - P

  • Linkedin

    • Likes / Comments - P

    • Competitor Likes / Comments - P

    • Thoughtleader Likes / Comments - P

    • Viewed Profile - P

    • Page Followers - P

    • Competitor Page Followers - P

    • Posted about the topic you solve - P

  • Follow Up / Delay - C / P

• Derivations

  • Closed Lost Follow Up - C

  • Closed Lost Now Can Help - C

  • No Show - P

  • Referrals - P

Optimization

Start by stringing together these tools, one signal at a time, until you have built a system that works for dozens of signals. Once you build a system that works end to end, now you can focus on some more complex topics, like

• Account Scoring

  • Every time a new company is added to CRM, we should pipe this information into Clay and score it based on a number of criteria, then this information and the results are piped back into our CRM. Assign a number of points to each category, and then score it.

  • Firmographics - 20 pts

    • Geography

    • Industry/Sector

    • Headcount

    • Revenue

    • PE Backed

    • Headcount growth

    • Number of GTM folk

  • Technographics - 40 pts

    • CRM

    • Using Competitor

    • Website Scrape Relevance

  • Intent Signals - 40 pts

    • New GTM Leadership Hire

    • New hire

    • Website visitor

    • Job posting

    • LinkedIn like

    • LinkedIn comment

    • Downloaded content

    • News announcement

    • 10K / Earnings Data

• Warming & Reputation

  • Use a variety of warming providers and reputation monitoring tools like MXToolbox and EmailGuard

• A/B testing copy

  • Use spintax and sequencing tools to try and prioritize different sets of copy for every signal

Setup Tips

There are a few tips I often find myself sharing that i’ll use this section to document

1. Don’t send emails from your main domain, only send from secondary domains

2. Don’t send more than 25 emails per day per mailbox

3. Do not add images or attachments to your emails

4. Do not track opens / make sure to remove the open pixel, this hurts deliverability

5. Make sure to include 2 variables in every email minimum

6. Leverage spintax as much as possible

7. Use a password manager like 1Password, it’s a gamechanger for speed. Make sure you add 2FA to the PW Manager

8. While I’ve seen differing accounts, don’t put more than 5-10 mailboxes on a given domain

9. Make sure to buy your mailboxes from Google or Outlook directly, and weight more of your mailboxes towards what provider your buyers tend to use. Industries that tend to lag with tech innovation tend to use Outlook, while newer ones use Google.

10. Warm your mailboxes for 2-3 weeks minimum before sending a single email

11. Add profile pictures to all mailboxes

12. If using Clay, use an API key for OpenAI as opposed to Clay managed OpenAI API Key, its substantially cheaper.

    1. Do the same for ZoomInfo if possible

13. If your ICP lives in Microsoft Outlook, send from Outlook mailboxes, if they use Google, then use Google mailboxes

14. Make sure you properly setup DMARC, DKIM, and SPF records

15. Setup email forwarding or leverage a unified inbox experience

16. When you first start, buy 5-10 domains just so you have them and can let them age :)

17. Don’t put more than 2 links in your emails

18. Keep emails short, no more than 3-5 sentences

19. Make sure to include a simple CTA

20. Cut off poorly performing sequences after 2K sends

🤫 The Secret - Generate Your Own Signals 🤫

Many teams focus their time and effort on spinning up a number of 3P signals (most of which are cold by nature) - things like job postings, tech stack, paid ads, all types of things that people at a prospective customer may decide not to respond positively to.

The real thing I push founders and growth teams on is to do the work to generate your own 1P signals. Things like

• Pushing more people to your website via SEO and AEO / GEO

• Building a brand and thought leadership to build a following for yourself and the Company

• Paid marketing

• Building community and organic referrals / PLG

• Product-led signals

• More surface area on website

  • Downloadable Content - guides, whitepapers, research reports, benchmarkings

  • Interactive ROI or pricing calculator

  • Webinars

  • Free Tools

  • Live chat / chatbot engagement

  • Surveys

  • Events

• Partners

  • Webinars, Podcasts, Guest Posts on Others

These methodologies will funnel more leads into your strongest converting channels which as a brand you should always optimize, your website and booking flow!

Ping me if you wanna jam!

Genuine question here:

Is it possible to build a successful company in the recruiting space that actually delights people - helping recruiters find and hire talent, and candidates find roles that excite them?

Recruiting is one of those problems that never seems to get solved. Everyone agrees it’s broken - startups struggle to attract the right talent, candidates drown in noise, and recruiters are caught in the middle. We’ve had two decades of LinkedIn, billions poured into ATS systems, and a wave of new hiring startups - yet the experience still feels frustrating and fragmented. So the real question is: what actually works, and why does it feel so inconsistent?

The Two Sides I See Every Day

A handful of my posts on LinkedIn these days have been about finding your next gig and connecting friends to open roles. It’s honestly sad that I know two fundamentally siloed parties that I can’t connect easily:

1. Startups solving large and rewarding problems, but either A) struggling to get applications or B) drowning in too many to sift through. Companies like Hebbia, Tennr, Legora, Harvey, Harmonic, Permitflow, Footprint, Owner, Rogo, Unify, Clay, Pylon, and dozens more.

2. Candidates from my banking days, Duke days, and even younger kids who DM me now because they want to make the jump into startups. Unfortunately, a lot of them occasionally a) lack initiative or persistence, b) are risk averse, or c) aren’t truly ready to take the leap.

From the candidate’s perspective, the journey is just as painful. You send 100 LinkedIn messages or Ashby applications and hear nothing back. You can be perfectly qualified, but you’re invisible unless you have a referral or a personal connection. What candidates want isn’t just more job postings - it’s signal. They want to know which startups are actually worth the leap, and they want a warm path in. That’s why brand and network matter more than any polished careers page.

People Choose Startups for Different Reasons

People join companies for different reasons depending on stage IMO.

• Pre-Series A: People join out of pure passion for the team and vision.

• Series A–B: Candidates want early-stage experience, career mobility, and a bit more stability.

• Series B+: It’s about hypergrowth, brand, and comp.

• Series D+: You’re basically a corporate drone (lmao jk but not fully).

At each stage, companies hire in different ways:

• Pre-Series A: Mostly high-initiative people who can articulate why they care, or referrals from scrappy agencies (TCP, GTMBA, Bleecker Street).

• Series A–B (toward PMF): You need little soldiers to brute-force repeatability. Recruiting is usually outsourced to multiple agencies.

• Series B–C: Repeatability and speed matter. At Unify, we had every employee sit down and mine their networks for the smartest people we knew. That built a strong referral pipeline. Agencies struggled at this stage. If lucky, you add your first internal recruiter.

• Series C and beyond: Recruiting stabilizes into a mix of in-house recruiters and agency support.

Why Hiring Feels So Hard

Because there’s no consistent methodology. Unless you decide to make hiring an internal priority, you’re winging it stage by stage.

And the tools we all use weren’t built for this. LinkedIn is noisy and full of spam. ATS systems like Greenhouse and Ashby are designed for process, not connection. Even Slack groups and Notion lists end up being glorified spreadsheets. The reality is that when I try to connect someone to a startup, it doesn’t happen because of software - it happens because I personally DM the founder, or I know someone on the team. The tech hasn’t caught up to how hiring actually happens in practice.

The Real Unlock: Brand

My hot take (which actually isn’t hot whatsoever): the best way to set your company up for optimal hiring outcomes from the very beginning is to make brand (and network) the centerpiece of your GTM and hiring.

We’ve seen what happens when a company nails its brand. Look at Pylon, Linear, Clay - their content and design make them aspirational places to work. Candidates line up because they want to be part of the story those brands are telling. On the flip side, when a company has no brand presence, recruiting becomes a grind. You default to agencies, outbound sourcing, or endless filtering through applications. Same market, same comp bands, completely different hiring outcomes - all because of brand.

Today we see companies like Pylon, Clay, Cluely, Icon, and more captivating people with dangerously entertaining content. I will actually pause in the middle of my workday to watch the latest Roy Lee shit-post or see Yoona Kim and Marty Kausas cook up a beautifully choreographed Pylon feature drop. We’ve even seen Adara Parker start to do this at Unify. It’s fun, it’s interesting, and most importantly: it makes me want to work there.

My Accidental Recruiting Marketplace

I know this firsthand because I’ve accidentally been running a mini recruiting marketplace out of my inbox. I have a running list of 50+ startups I admire and 75+ friends or acquaintances who want to break into startups. Every week a number of people ask me for an intro. It’s objectively not scalable and there’s no tool that makes that simple - it’s all trust and brand. And honestly, that’s the lesson: recruiting works when people trust the connector and when the company’s brand is strong enough to get people excited. Everything else is noise.

I remember seeing one of the recruiting SaaS founders share years ago just how difficult it is to build successfully in this space, because of the fragmented stakeholders and preferences. That’s still true today.

The Challenge

If you’re building a startup today, your real recruiting advantage isn’t comp, perks, or headcount - it’s whether people think you’re cool enough to join. That might sound superficial, but it’s the honest truth: the companies that win talent are the ones people talk about, not the ones with the slickest ATS.

What if we rebuilt recruiting from scratch with brand + network at the center instead of treating them like afterthoughts? My bet is that the companies who do that will find hiring not just easier, but genuinely delightful.

So if you’re a founder, forget the ATS for a second. Ask: does anyone outside your team want to work at your company? If not, that’s the real recruiting problem.

The Solution?

What could the solution look like? Well, maybe something like:

• Candidates have a curated way to discover startups that match their risk appetite + values.

• Founders can tap their networks in structured ways, not random spreadsheets.

• Recruiters evolve from middlemen to brand amplifiers / community builders.

If you’re trying to solve the hiring / recruiting problem, please reach out - i’d love to hear about what youre building and help to the extent I can!

Garrett

For some, joining a startup is as far as their risk appetite will take them. But for others, they may start to feel an itch, and that itch begins to grow. I got that itch. I have friends who have gotten that itch. And the question you are probably asking yourself is

When and how do I know to scratch that itch!?

Starting a company is no small undertaking, so it is understandable that people want to make a really educated decision on how / when to do it.

Before we even get into it, the TLDR is that there is never really a good time to do it.

The reality is, you need to take a leap of faith. But in this little stream of consciousness piece I wrote below, I’ll try to walk you through my journey (and that of my cofounders as well), so you can see how we established the foundations of our shared vision for Galaxy.

Identifying a Problem

The first step in starting any company is identifying a problem you want to solve. When trying to do this, think deeply about your day-to-day workflows, areas you’re interested in, and more. Outside of your experience, talk to friends, family, colleagues, and more - there is a wealth of companies waiting to be built - it’s no coincidence we see so many companies started every year.

There are a few routes you can go when identifying an area:

1. Identifying a problem you have felt personally

2. Identifying a problem someone else has

1. Identifying a problem you have felt personally

Trying to solve a problem you have experienced firsthand is the most compelling reason to start a company.

There is a concept in the VC world called founder market fit → this is a heuristic used to describe the relationship between the founder and the problem they are trying to solve. Is this person the right person to solve the problem? Typically, those are the founders that win, especially when there is competition, because they know what the product needs to be to be successful.

Mitch and Leon have worked extensively with data over the last 5+ years across teams at Bloomberg, Parkmobile, Flock Safety, and Unify. They have built, procured, and used dozens of data systems and worked with hundreds of other data practitioners. For them to team up to solve this problem makes a heck of a lot of sense.

For me to start a company like Galaxy without them would not make a ton of sense :)

We see this theme of founder market fit over and over again:

• Austin building Unify

• My buddies Brendan and Branick in building Aryeo

• and dozens more

2. Identifying a problem someone else has

While this one is a bit more difficult in execution, it has become more common over the last few years. Jump into a random field that desperately needs innovation, shadow real people in the space for a few months, and observe their workflows, and create a solution to solve their problems. Do market research or go talk with people that you want to serve and figure out how to improve their day-to-day.

Mitch and I have had dozens of ideas that we have had or know others have. Listing a few of our ideas below:

Garrett - a better workflow platform for recruiters / headhunters (I hate their poorly fit automated emails and job recommendations), a financial wiring platform for large financial transactions, etc.

Mitch - a platform that allows you to bake PLG into your product as PLGaaS, a 24/7 worldwide gameshow with prizes, etc.

Leon -

Selecting a Co Founder(s)

I’m not saying solo founders can’t win. Plenty have. But if you're reading this and don’t have a chip on your shoulder the size of a small boulder or a cult following on Twitter - you probably want a cofounder.

Cofounders make the highs higher and the lows survivable. They keep you honest when you're too hyped about a feature no one wants. They push you when you're spiraling over a bug no one will notice. They’re your mirror and your momentum.

So how do you choose one?

Here’s the framework I’ve come to believe in:

1. Would you be friends even if this failed?

   • You’ll be spending more time with this person than anyone else. Do you actually enjoy hanging out? Are they someone you'd grab a drink with after a brutal day?

     

2. Complementary skill sets

   • Can they build what you can’t? Sell what you can’t? Think about this like building your own Avengers squad.

     

3. Mutual respect for each other’s spike

   • You don’t have to agree on everything. In fact, you shouldn’t. But you need to genuinely admire what they’re great at and vice versa.

4. Same level of burn-the-boats energy

   • You’ll hit moments when everything feels like it’s going to implode. Does this person still show up with the same fire on the hard days?

5. Someone you enjoy talking to casually

   

Navigating Cofounder Conflict

Every cofounder relationship, no matter how strong, will hit moments of friction. That’s not a red flag - it’s the cost of building something that matters. What matters is how you work through it. For us, we committed early to directness: no side conversations, no backchanneling, no simmering. If something feels off, we bring it up - even if it’s awkward. We also try to assume positive intent. If someone’s pushing back, it’s because they care. One thing that’s helped a ton: separating what we’re deciding from how we’re deciding. Sometimes the disagreement isn’t about the idea - it’s about how we’re making the call. Having a clear framework for how decisions get made has helped us move fast without fracturing trust. The three of us have had disagreements and worked thru them with time and care. And as time goes on, I trust that all three of us will continue to prioritize our shared relationships :) I also go to therapy to try to just be a better person, and that certainly helps. I highly recommend it :)

Leon, Mitch, and I complement each other really well. Mitch and Leon are both incredible engineers with deep experience in the space, and I’ve spent my whole life around GTM, investing, and company-building. We’ve built mutual trust and have hard conversations head-on. And we really like working together, which might sound fluffy but turns out to be one of the most important parts. Mitch and I are very high energy and passionate, and Leon is extremely grounded. It’s a great combo.

Mitch absolutely refuses to add some fun colors to Galaxy after the AI copilot generates SQL. I’m going to get him to swing in my direction soon :)

Validate the Problem

It is of the utmost importance to validate the problem and its severity before you go gung-ho on building a solution. Talk with people in your network who may be familiar with this problem or feel it directly. Ask them:

• Is this a problem for you?

• How painful is it?

• Why hasn’t someone solved this problem to date?

• Is this a problem you would like solved?

• How much would you be willing to pay for a solution?

Being a less-technical member of the team who hasn’t felt the problem to the same extent Mitch and Leon had, while also being a ex-investor who generally is skeptical of dev-tools, I immediately started doing work to ask people I know about this problem.

In the days after my first conversation with Mitch, I setup time to chat with dozens of friends from college, ex-coworkers and more. People like Brendan Quinlan, Branick Weix, Andrew Arnold, Luis Fornes, Riley Cohen, Garrett Neff, Riley Hockett, Billy Ramundo, Elie Farah, Stu Baker, Liran, Matt Gigolio.

Anyone that had a point of view of exposure to the problem space I wanted to talk to. We recorded all the calls and began to build an understanding of what people were and were not frustrated by, and what a solution could potentially look like.

Brainstorming a Solution

Now it’s time to be really thoughtful and brainstorm a solution. Take time to do research and draw up what you think a solution could be. Talk with real stakeholders and see what they might imagine a solution could be. Begin to outline the answers to a few key questions

• What is the name of said solution?

  • This is by no means a p0 level priority, but it sure as hell is fun to begin to talk about the idea with a name.

• What does this solution do?

• What does the solution look like and how does it feel?

• Who uses this solution?

• What are the key benefits, and what is the expected ROI?

  • People typically only pay for things they can justify the cost of - it is important to try to articulate the value of the product in relation to what the cost is.

    • i.e. Our product saves you 20 hours per week / $2,000 per month / 3x more efficient

    • i.e. Our product creates 4x as much value as it costs in just the first week

• How much does the product cost?

• What competitors are out there?

  • It is critical to map out who you will compete with today, and who you might compete with in the future.

  • Sometimes competition may be strong enough that you may determine not to pursue this idea at all

  • This is the very beginning of mapping out your Competitive Landscape - which is useful for articulating to investors, to customers, and even for sales processes down the line

• How many people are out there that would be a good fit for your product?

  • This is the very beginning of mapping out your TAM - or total addressable market

• How are you going to distribute the product and sell it?

  • If a product is impossible to distribute, you probably can’t build a company out of it :)

Mitch and I had been talking about the idea that was Galaxy very casually but eventually one Saturday we met up at L’antica da Michele in the West Village with our third co-founder Leon to discuss the idea and what it would look / feel like. Over some pizza, pasta, and espresso martinis, we talked through much of the above. In the days that followed we ironed out many of these areas and planned how we would make this vision come to life.

Gauging Investor Interest

Some problems require venture capital funding to solve. I say “some” because not every problem requires millions of dollars of backing and hypergrowth.

A super quick lesson on VC firms and how the make money. Venture capital firms make money by investing in the next great companies and those investments (companies) becoming liquid at some point, either through an acquisiton, or ideally - IPOing and going public!

Venture capital investing is extremely risky. 99.9% of all startups fail, and so for the most part - any investment that a VC makes is probably going to go out of business. VCs know this (and plan for it), and as such hope that 1-2 of their investments create all of their profits.

This is called Venture Power Law. VCs are in the business of betting on the next big thing. It doesnt work for them to pick a bunch of small positive outcomes - they need the enormous ones.

This is important because if your idea has a total addressable market that is too small - venture capital firms may not be wiling to invest in it, and thus you will need to bootstrap the company yourself (meaning you probably can’t pay yourself a salary, hire, spend money on things, etc.).

At a certain point post brainstorming the solution, if you are planning on quitting your job and starting this company full-time, assuming you want/need to pay yourself a salary - you probably need to raise money from VCs in order to do so. It may make sense to have some very casual conversations with venture capital firms to also validate your idea and see if they’d be excited about it.

Venture Capital is a relationship-driven business - reach out to friends, family, and others who might be connected with various VCs to have a conversation with them. If you’re interested in meeting with VCs and want help getting in touch with them, feel free to reach out to me on Linkedin or at my email and i’d be happy to help out.

VCs will be very direct - prepare for these conversations and take notes on their questions. They have experience listening to thousands of pitches and its likely they’ve seen an idea like yours in the past. Prepare for them to ask deep questions on the problem-space, the market size, and even go-to-market strategy.

Given my venture background I had spoken with a number of friends in the VC world to gauge interest and determine if Galaxy would be interesting.

We spent a considerable amount of time working on, practicing, and refining our pitch, especially after conversations with VCs, customers, and more to double down on what was resonating.

Making the Decision to Quit & Build Anew

In the first copy of this blog I debated putting this at the end, but I felt bad for making people read all the way to the end before talking about one of the most interesting parts of this piece.

For most people, they will be unable to do the portions of this post that come after this section (building, marketing and launching) while still at their normal day job. It’s not only tough to find the time to build and sell, but you also will run into roadblocks from your existing employer.

Thus, at some point, you will need to make a call. If you were reading this in order to get the answer to your burning question “how do I know when I should quit and go out on my own full time?”, I unfortunately don’t have a good answer for you (lol)

The brutal reality is that no matter how much discovery, building, and validation you do - startups are risky for a reason - there is never certainty. All you can do is develop your own excitement and conviction, find a great team to build with, and a handful of other small things. Then, you have to go with your gut and take the leap of faith.

That said, there are a few questions you can ask yourself in the final inning and are worth discussing with your cofounders to ensure you set yourself up for the best odds of success:

• Have I budgeted to ensure I can bootstrap this company and my lifestyle over the next 6-12 months without a steady source of income (assuming you don’t take / can’t get VC backing immediately)

• Have I outlined the product roadmap such that I know what I need to build and how long it will take?

• Have I identified the beginnings of a repeatable GTM process that I can invest in as we build and market this product?

• Does there appear to be some investor interest in an idea in this space?

Path to the MVP

Once you identify the problem, validate it, and brainstorm a solution - it comes time to methodically plan out what a minimum viable product (MVP) could be. This is your best way to test the waters and determine if what you are building is of value to the people you intend to sell to. As a team, sit down and think through what the path to building an MVP looks like

• What are the minimum set of features this product must have in order to validate our thesis and be used by customers

• How long will it take to build?

• How much will it cost to create?

• How do we expect prospects to react and what outcomes would validate or kill the idea?

This portion was predominantly comprised of Mitch and Leon discussing what we wanted to build and how we were going to build it. Once the initial roadmap for the MVP was planned, together we went super deep on each feature, how necessary it was, and any risks we could foresee inherent in it.

We actually put together an extremely thorough spec of each phase of building, how much time and people it would require, expected $ cost, and the key features to be delivered. We also identified each risk we might run into.

De-Risk

The best teams divide and conquer responsibilities. For many of you reading, you’re probably wondering how to de-risk your decision to start a company. My recommendation is to create a team comprised of someone who can build, and someone who can sell. While person A is building, person B should de-risk the build by continuing to validate the problem and also pre-emptively marketing and selling the solution.

The last thing you want is for your hardworking technical cofounder to spend months building a product only for you to then have to go find people to sell it to and use it.

How do you sell someone something before it exists? This is the art of selling - people have been selling promises and vision since the beginning of time. There is always “the next thing to build” and the best sales people can sell it before it even exists :). Begin to push on your go-to-market strategy to drum up early interest in your product, so that when the MVP is complete, you can get these early users onto it.

So how do we do this? There’s three main components: find people to sell to, create things to show them, and offer early adopter perks.

Find People to Sell to

1. Leverage your network

   1. Reach out to friends, family, school alums. Share your vision for the product and get them excited about using it.

   2. Use things like LinkedIn, ask for referrals and intros

   3. Do not underestimate network, it is commonly the methodology through which founders generate their first million dollars in revenue. While your company is just an idea, you can’t expect people to just find you on the internet!

2. Search through communities

   1. Get involved on social communities like Reddit, Twitter, Linkedin groups, or even Facebook groups or Whatsapp Chats. These are areas where their is organic conversation from real stakeholders in your space.

3. Do 1:1 founder-led outbound

   1. Identify people who might be interested in your product and start doing personalized outbound to them. Some of your earliest users may be your biggest fans, its important to get them onboard and familiar with you for support :)

4. Go to events

   1. Consider attending some low-stakes conferences, networking events, or other places where your buyers might be.

5. Start posting on socials

   1. See if you can begin to build a following and generate interest in your mission and vision.

We started by leveraging our networks and reaching out to devs we know. We then moved to tap Alumni networks and even looked thru Reddit communities. Then we literally started reaching out to random people and tried pitching Galaxy to them. Hundreds of our customers today are people I cold DM’ed on Linkedin :)

Create Marketing Collateral

1. Build a website

   

   1. Give people a place to find you on the internet where people can see your idea, sign up for a waitlist, and share your idea with others.

   2. SEO / AEO is a potentially large component of this; if done correctly, you can begin to create a website that people can find organically on search engines and answer engines. See my post on AEO / GEO

2. Create marketing materials

   

   1. Consider creating decks and one-pagers that

      1. show any designs if you have them

      2. communicate your vision

      3. give them ways to get in touch with you

      4. and are shareable!

Our team spent a few grand iterating on designs with a contractor. This was really great for showing to prospects and getting people excited. I would definitely recommend building doing something like this. I also began to explore Webflow and how to build a website we could point people to. This was a really big value driver for us.

Offer Early Adopter Perks

Most early stage teams need to give their products away for free at the beginning to get feedback. If you’re lucky, some customers feel the pain point you are promising to solve so severely that they will actually agree to pay for a solution.

If you’re super lucky, some customers will agree to be Design Partners - which is an actual contractual agreement that says a company will be customers of yours for some set period of time and will give you logo rights and a case study in exchange for early access to the product and new features, priority support, and some discount on the full product once the design partnership is over.

Use templates like the Common Paper one to structure formal relationships with your Design Partners :)

Myself, Mitch and Leon have spent hundreds of hours on the phone with developers and data practitioners sharing our vision for the product and trying to convince them on being a Design Partner.

To do this we would spend 15-30 mins on the phone with them doing discovery, sharing our vision, and if they were a really strong fit - we would then proceed to ask them to be a Design Partner and tell them they would get free access to the product, extra AI credits, and other perks.

Plan Your Launch

This is the fun part. It’s time to plan the launch! Launching your product is a huge milestone, and one that you and your team should celebrate. But before you do that, there’s tons to do. With any launch, you want to help your product really hit takeoff velocity and give it the best chance of having a viral launch. Achieving such an outcome does not take luck - its actually tons of preparation and meticulous planning. Some things to consider planning out:

1. Pressure test the product for login, auth-flows, and any high touch areas of the product to ensure for a smooth first impression

   1. To account for this, I recommend rolling the product out to a super small group of users (3-5) that will pressure test the product

2. Polish your website, signup / download flows, and create some screengrabs / videos using tools like ScreenStudio :)

3. Launch announcements across socials - Linkedin, Twitter, Reddit, ProductHunt, etc.

   1. Ask design partners (both teams and individuals) friends, families, colleagues, VCs to support, engage and upvote to place high in the socials feeds

4. Plan what initial support will look like

   1. It’s super crucial to plan the process for reporting, triaging, and fixing customer bugs. The last thing you want is for customers to have a bad CX and then churn since they dont feel like they are being prioritized..

Our team has spent countless hours talking about launch. Things like our marketing, social posts, and more have been talked about endlessly for hours and hours upon end.

Don’t skip this part and really tap your network to help support you and engage with your posts as much as possible. You only have one first launch!

We actually did a poor job determining the level of support we would be able to offer to customers during our Alpha launch and also let in too many individuals to the product. This created a sub-par onboarding experience and one we definitely learned from. Going forward, I mapped out what customer success and support would look like, as well as planned which users would get access to the product in phases.

Iterate Fast With ICP Customers

Once you’re through the initial launch, it’s important to collect feedback but also identify and choose the customers that you want to build for. When I imagine this, funny enough I think of entering a gravitational slingshot. As tacky as it sounds, you want to identify your best fit customers, and iterate with them super quickly. Collect feedback as quickly as possible, ship fast AF, and get them a product they’d die to have sooner rather than later. This is absolutely key and you want to turn them to evangelists :)

Failure & Exit Criteria

This part rarely gets talked about, but it’s something every founder should think about. There’s so much survivorship bias in startup land that we all end up romanticizing sticking it out forever. But knowing when to stop - or pivot - is just as important as knowing when to start.

There’s no perfect formula, but here are some signals I think about:

• Are people actually using the product?

  • Not just signing up. Not just telling you “this is cool.” Are they using it without you bugging them?

• Are people paying for it?

  • An obvious one, but businesses can only be businesses if they will be paid for.

• Are you learning new things each week?

  • If your feedback loops are flatlining, that’s a red flag.

• Have you been chasing “just one more feature” for too long?

  • Sometimes this means you’re stuck trying to build your way out of a bad idea.

• Is your team still aligned?

  • If the cofounder relationship is breaking down or the team is pulling in different directions, it's worth pausing and reevaluating.

• Would you fund this again knowing what you know now?

  • This is one of my favorite mental checks. It removes sunk costs from the equation.

We didn’t write out hard exit criteria at the beginning - but we probably should have. Even a rough sketch would’ve helped us frame decisions better. That said, we constantly gut-checked ourselves on whether we were seeing the signals we wanted to see from users, and that helped guide our next steps.

We did however write out what we would need to see to make the jump - tons of interest, hints of investor taste, and a well paved roadmap and way to get there.

The Emotional Reality

No one really prepares you for the emotional whiplash of starting a company. One day you feel like you're building the next unicorn - the next, you’re spiraling because someone didn’t respond to your email. It’s a constant push and pull between belief and doubt, momentum and inertia. The highs are higher than anything you’ve felt in a normal job, but the lows can be isolating and brutal. I’ve had mornings where I felt unstoppable and evenings where I questioned everything. You have to build a kind of emotional endurance - learning to stay steady through both chaos and silence. Surrounding yourself with people who remind you why you’re doing this (and who still like you even if the idea flops) is critical.

I remember when we first sent our deck out to VCs and I literally laid down on my bed and was freaking out and forecasting our odds of failure; like

• “oh shit theres no turning back now”.

• “how the hell did i settle on this idea”

It was a hilarious reality of building.

I am nowhere near being successful just yet, and the odds of failure are high; but its about the journey and having a good time while building something that matters!

Fundraising Strategy (and What Actually Worked)

Fundraising is one of those things that looks easy from the outside (“just raise a pre-seed!”) but is wildly time-consuming and emotionally exhausting in practice. Everyone’s winging it, even the ones who look like they’re crushing.

So here’s how we approached it:

1. Warm up your network early

We didn’t wait until we had a deck to start talking. I started reaching out to VCs I had relationships with from my investor days and just… floated the idea. Casual “hey, we’re noodling on something” coffees. It gave us signal on who was actually excited about the space.

1. Make a deck, but know it won’t matter much

You need a deck because VCs need something to forward to their partner. But the best conversations don’t follow your slides. They follow your energy. Your conviction. Your story.

1. Tell a big story, but ground it in now

A lot of people try to pitch a $10B vision without showing any path to $1. We tried to balance: here’s the world we see, here’s how we’ll get there, and here’s why we’re the right team to build it.

1. Stack your meetings

FOMO is real. Try to have all your VC conversations in a 7–10 day window. The more things overlap, the more momentum you build.

We were lucky to get a few strong signals early from VCs who had conviction in the space. I leaned hard on warm intros and clarity of story. I also made sure to follow up fast - investors are evaluating how quickly you move just as much as the idea itself.

If you’re fundraising, feel free to DM me — always happy to help with intros, deck reviews, or just talk strategy.

Mistakes We Made (So You Don’t Have To)

You will make a thousand mistakes. That’s the deal. But here are a few we made that might save you some pain:

1. Letting in too many alpha users too fast

We got excited. People wanted to try Galaxy, and we said yes to a lot of them. The result? A flood of feedback we couldn’t act on fast enough and onboarding experiences we weren’t proud of.

➡️ What we’d do now: Start with 3–5 users, perfect the loop, and then widen access.

1. Trying to do too many GTM motions at once

LinkedIn, Twitter, Reddit, SEO, cold outbound, community — we tried to spin up all of them fast. Which meant none of them were amazing.

➡️ What we’d do now: Pick one or two GTM channels and go deep before layering in more.

Conclusion

As the Twitter and LinkedIn shitposters say (and lowkey the guys at Cluely):

You can just do things

Go out and do them

Now, let’s get into it.

Over the course of my career I’ve worked at 3 (now 4) different companies and coached friends, friends of friends, and clients into roles at all types of companies from investment banks to private equity and VC firms, to dozens of startups. I’ve always been a fan of helping others, and have spent hundreds of hours coaching others, networking, and helping others land jobs that they love and are excited about. There is nothing that has brought me more joy than helping people I (and others) care about, finding fulfillment in the workplace.

I’ve put together this document to help document my knowledge and provide it as a resource to aspiring professionals who want to figure out what’s next.

A little on me if this is your first exposure to me:

Duke 2020, Morgan Stanley Tech IBD, TZP Growth, Unify first Growth / Bizops hire (employee #9)

extremely talented pasta chef, nyc restauranteur, movie enthusiast, marvel adult

https://www.linkedin.com/in/garrettawolfe/

Intro - a quick lesson in finding your way in your career

Since I graduated college I’ve had the fortune of seeing many different industries; investment banking at Morgan Stanley (Tech Group), software investing in growth equity / VC at TZP Group, and most recently Growth / Bizops at a startup called Unify.

When I graduated from college as a CS major I frankly didn't have the initiative to start my career in the Software Development space, it was too foreign to me and the investment banking path was so clearly laid out. Funny enough, when I was an intern at Morgan Stanley I actually thought I would be a career banker because I loved the responsibility and my team (I was wrong however, I didn’t feel fulfilled by the nature of my work).

When I joined Morgan Stanley full time, I was starry eyed at the thought of working in Tech VC. When I joined VC, while it was exciting to some extent, I wanted to move faster and be inspired and build. While this could’ve been a function of the firm I was employed by, I do think there are some fascinating nuances to VC and the industry that pushed me away from investing.

When I joined Unify, for the very first time in my life I felt like I truly was excited to get out of bed everyday and to go to work. I was excited to get to interact with my coworkers in the office, I was excited to learn and help drive outcomes in every part of the business, I was excited to help our team win and grow.

I joined as employee number 9 of Unify back in April of 2024, and at the time of writing this (July 2025, only 16 months later) is now north of 50 people. Things have changed pretty significantly since I joined, and my roles and responsibilities had changed dramatically too by the time I entered my last few weeks at Unify!

I say all of this to emphasize four key points which matter a great deal:

1. Your career journey isn’t linear and it takes time to find what you love doing. You may think you are going to love something and for whatever reason you don’t! Keep searching and trying new things until you nail it, your experiences will compound and allow you to join any organization as a more-informed and and more confident individual. It is never too late to try something new

2. Take a risk! We all can get so easily locked into working at big companies that promise a steady income and a big brand name. I was so exhausted of being a corporate drone and never feeling any purpose or fulfillment during the 8-12 hours I worked each day. Take that leap of faith and do something that makes you excited (and nervous)!

3. I truly believe that the velocity of learning and growth is exponentially faster at smaller organizations, more tech-forward organizations where you have the ability to do and own more than at large firms / with older teams. I have learned more in the last 15 months than in the last 5 years at my other 2 jobs.

4. Things change! Teams grow and shrink, people come and go, competition grows, priorities change, and life happens. Be comfortable with change and the need to adapt and find your path forward.

What is it like working at a startup?

Startups (especially earlier ones) give you an incredible amount of agency. Gone are the days of waiting days for comments on a deck from your boss or the client. Excluding what is expected of you contractually, you are the owner of your own time, the manager of your projects, and the executioner of what you decide you want to do every day.

At startups, there is always something to do, and more commonly - there’s always something that you can add to your plate to help your company win even more. It’s not an easy task, but it’s an intoxicating and exciting feeling that I am beyond grateful to have in my career that has reinvigorated my work days (and even weekends) with a sense of urgency, importance, and desire to do more.

With that out of the way, many of you are here to learn about how to get a role at a startup. But before we jump into it, there are a few things I should warn you of.

1. Working at a startup is hard - the hours are long (I frequently work weekends), the work can be grueling, and the risks are high.

   1. If you want to feel purpose in the workplace and having agency of what you do excites you, it’s all worth it.

2. There is a high degree of risk in working at a startup - the vast majority of startups shutdown, and your equity will be worth nothing.

   1. However, the experience you have and the things you learn are multiples more important than any opportunity cost of working in a typical corporate role.

3. Startups can be emotional - when every day passed is one less day of runway (the time a company has till it has to shutdown), people can get emotional. Startups are high velocity and things can change on a dime.

   1. It is your job to find people who you believe will handle adversity and hardship in a mature and professional way. And for some, this stress / concern is a great channel to motivate yourself to get shit done.

How to get a startup job

Like anything in life, there is no shortcut or cheat code to get a job at a startup. It’s actually quite a complex process and involves quite a bit of thought, planning, and initiative. And I promise, the quantum of effort you put in, will directly impact the quality of the jobs you get.

Looking Inwards

Finding your next gig takes time - you need to be aware of this and understand that finding any gig, let alone one that you would be over the moon about, takes time and effort. When I decided I wanted to go to the startup world, it took me 15 months to nail my role. It took me three months to start getting interviews, another 5 months to start getting offers and another 4 months to really start nailing the selection and interviewing process. By month 15, I was fortunate to have lined up a number of companies who were excited at the prospect to work with me. This isn’t a brag - it takes substantial time and effort. Why does it take so much time?

1. Self awareness and understanding what you want - Any time I chat with someone who wants help figuring out what’s next, the first question I ask them is “what do you want to get out of this move?” The reason I ask this is two fold:

   1. In order to effectively and efficiently look for something that you want, you need to identify the criteria that make it a qualified opportunity! If you don’t know what it is, you’ll probably never find it!

   2. In order to get a job, you need to be able to articulate what you are looking for in a company to a founder or hiring manager, what you want to do day to day, and why - and you need to convince them of it! Humans are terrible liars and the less conviction you have in yourself and what you want, the more obvious it will be in interviews. The questions you need to ask yourself are:

What do I want?

1. What industry do I want this company to be within?

   1. Tons of people say they don’t care what their employer does, but if we’re working at minimum 40 hours week (definitely north of 70), why wouldn’t you care! We want everyone to be excited about what they do every day and the mission the company has.

   2. Answer this question by selecting areas that are interesting / exciting to you.

2. What stage company do I want to work at?

   1. The earlier the company, the more risk, the more reward, the more casual, the longer the hours, but the more agency you will have.

   2. You can answer this question typically by looking internally and asking how much risk you want to take, what your working style is, and how fast you want to move.

3. What role do I want to have at the company? What do I want to work on day to day?

   1. The roles and responsibilities of people at a company can vary quite significantly. There are differing qualifications by role, and what you will do will vary as well.

   2. Answer this question by reflecting on your background and find a combination of what you want to do and what you’re qualified to do.

4. What time horizon am I looking at?

   1. Based on where you are in your life you may be looking for different things? Are you young and hungry? Do you need to start a family soon? Do you have kids already? Crazy, but this is important to think about.

5. Cherries on Top: Once you nail the above categories, you can also optimize for a few more factors

   1. Locale - where do I want to work? Am i comfortable moving?

   2. Working style - are you the type of person who likes to be told what to do and then you go and do it? Do you like to work on projects solo and be your own manager?

   3. Work Flexibility - Do you want to work in office 5 days a week or have a mix of remote and in office? Learning is much faster in real life. Some startups now expect 6 or more days in office!

   4. Backers / Investors - the best companies and the best people are typically (not always) backed by the best venture capital firms. This tends to be a good signal for you to explore as you research companies

   5. Perks - self explanatory

   6. Pay - also self explanatory but will note that you should not expect to be paid tons at Pre Series D companies in IC roles.

Looking Outwards

Ok, now that you’ve done some introspection and self discovery, you probably have some solid answers to the above questions! With this set of data, we can actually go out and into the world and try to find what would excite you!

Refine your resume

This is more nuanced but you should try to rework your resume to emphasize your skills that promote your

• Cross functional projects

• Side-gigs / side hustles that show initiative

• Areas where you’ve worked on projects that have tangible ROI / benefit to your organization

• Personality and hard-working nature

• Ability to be an individual contributor and a manager / coach

Emphasize / Create your brand

• Emphasize your personal brand if you have one or create one if you don’t! These things show initiative outside work and your ability to leverage your network. Things like starting a blog, building a portfolio, generating a following. This is what led me to start NextRound - to build my interest in startups :)

Tap your network (absolutely critical)

Before and during your search, reach out to friends, colleagues, and people in your network that may be able to help guide you through your search and learn more about

1. the roles you’re interested in

2. the size of companies you’re interested in

3. the industries you’re interested in

4. the types of problems you might be able to help solve

5. what they have learned and what you should expect

Build a list of companies

Use resources like Crunchbase, LinkedIn, Pitchbook, SourceScrub, Harmonic, Apollo, ZoomInfo, or any company data tool to create a list based on the criteria above. You can even use Perplexity or the LLMs now. You can add filters for

1. Size (# of employees)

2. Stage (stage of funding)

3. Industry

4. HQ location

5. Founding Year

6. Investors

7. Last Funding Round

   

Empirically, if you enter these filters into Crunchbase as I just did, we only get 27 results!!!!!

When you do the same search but anchor to San Fran, you only get 19!!!!!!

Lucky for you, this is your total addressable market, or TAM, of companies that meet your search criteria. The fact that its sub 50 is actually a good thing - it means you can spend time hand-researching each one and determining which you are the most excited about.

It’s time to do your own outreach!

Many friends and colleagues I’ve advised like to passively search for jobs by applying to job application they see on LinkedIn. I personally guarantee that you will almost never get an epic job via this type of initiative, and it will also take considerably longer. The hottest most exciting companies out there have dozens of people reaching out to the founders, putting together crazy case studies, or showing interest in bizarre ways. They also have headhunters / or recruiters working on their behalf, so you need to do your own outreach and make yourself stand out while doing so.

1. Use LinkedIn to find the relevant people to reach out to at a company and message them.

2. Use tools like Apollo or others (leveraging free plans) to find peoples work or personal emails to send them emails

3. In sending emails, find the messaging that works for you sharing

   1. your background

   2. why you’re interested in / excited by the company

   3. how you think you can help

4. Be sure to follow up and try to break in!

5. I recommend exploring using a Notion / Airtable / Hubspot / Google Sheets for keeping track of companies / outreach activities (all free tiers)

6. While I do recommend writing emails by hand to make it personal and stand out, I recommend using Hubspot or similar tools to use templatized emails (that you can further customize).

What do I need to know to succeed?

Once you’ve gotten in touch with the team and have shared your interest, the job is relatively simple from there, it’s all about working on your story and why you’re a good fit, conveying what you want to do, and managing a interview process.

1. Your story / fit

   1. Why are you interested in this company?

   2. Why does your background make you a good fit for this company and a good fit for this role?

2. What do you want to do?

   1. What do you actually want to do day to day?

      1. This is extremely difficult to determine without startup experience, but having ideas here is the best way to differentiate yourself. No founder wants to work with someone who “has no preference / doesn’t know what they want to do” or “wants to strategically tackle problems in a cross-functional manner” (i.e. they are being vague because they don’t know).

   2. What projects would you want to work on?

   3. What problems do you imagine this company has and how you would solve them?

      1. Once you can answer this, you are in a very good place.

3. Managing the interview process

   1. Having a perfect resume

   2. Sending thank you emails

   3. Being outgoing and following up, showing consistency

   4. Create a list of thoughtful questions to ask founders and hiring managers (e.g., questions about company vision, culture, runway, and growth plans).

   5. Negotiation

      1. Far more nuanced but worth exploring typical comp / equity ranges associated with different roles and different stage companies

   6. Getting answers to questions you should do your homework on

      1. How much money are we burning? How much runway do we have?

      2. What does my trajectory at this company look like? When will I be promoted if I perform?

Things to be cautious of

• Founders who are too young

  • Being a founder is hard - you want people who have a certain degree of maturity and experience working with and managing other people.

• Companies that have declining headcount growth (excluding the effect of AI)

  • Companies whose growth has slowed is typically an indicator of poor performance and company growth / health

  • Of course AI has made teams more efficient so something to weigh here

• Companies that have high degree of legal / market risk

  • It’s important to be cautious of companies that have inherently high degree of risk such as sectors that are relatively novel, involve lots of legislation, or are dangerous in nature. Examples of this may refer to crypto, AI (to some extent), drugs, etc.

• Companies that have questionable degrees of market traction / product market fit

• Companies that are in an intensely competitive space.

Things to ask and investigate

• How were customers acquired? Were they through natural methods or is a friend doing them a favor?

• Have the team demo the product to you and make sure it works

• Do your homework on past employees who have left the company - why did they leave?

• Get references on the founders - do people say good things?

• Find out if the sales team is hitting / beating quota

Appendix

Different stages of companies

In the below table, I try to share a rough breakdown of companies by stage, specifically for the technology / software space. This is not representative of every company in every space.

• Pre-Seed

  • All ideation and product discovery, focused on building

  • <5 people

  • No hierarchy

• Seed

  • Focused on building and exploring how to sell

  • 5-15 people

  • Still no hierarchy

• Series A

  • Scaling GTM and product development

  • 15-30 people

  • Early signs of specialization / teams forming

• Series B

  • Hiring Org leaders and scaling teams

  • 30-60 people

  • Formal reporting begins

• Series B - Pre-IPO

  • Growing explosively, becoming corporate

  • 60-2K people

  • Full blown orgs

• Post-IPO

  • Full blown bureaucracy

  • 2K+ people

  • Ooooof

Rough classification of roles

In the below table, I try to share a rough breakdown of roles at a company, specifically for people with Business / Consulting / Finance experience (backgrounds like myself). This is not representative of every industry and is limited to my experience.

• Account Executive (AE) | Sales Org

  • Alternative Names

    • Growth (at young companies)

  • Account executives exist to manage and close deals that are generated by new business teams in the form of pipeline. They are responsible for closing a certain amount of revenue each month. They receive less equity because they are typically paid tons in commission

  • Skills

    • Managing relationships, Multithreading, Owning a quota, People skills, Selling

  • Equity

    • Very little

• Sales Development Representative (SDR) | Sales Org

  • Alternative Names

    • Business Development Rep (BDR), New Business Rep (NBR)

  • Sales development representatives exist to generate new leads for Account Execs via cold calling and emailing. They are responsible for generating a certain number of new leads per month. They receive less equity because they are compensated well on leads generated. While SDRs are one of the most important roles at a company, they tend to be considered more junior in nature.

  • Skills

    • Cold calling, Cold emailing, Owning a quota

  • Equity

    • Very little

• Business Operations | Operations Org

  • Alternative Names

    • Bizops

  • Bizops was where I started at Unify. In this role, you’re a jack of all trades, juggling things like company finances, revops / CRM work, sales enablement, post sales C-suite assistance, case studies, event organization, and everything inbetween

  • Skills

    • Cross functional, Jack of all trades, Managing finances, Revops work, Sales enablement

  • Equity

    • Slightly more

• Growth

  • Alternative Names

    • GTM Engineer, Growth Marketer, Growth Specialist, Growth Operations

  • Growth is a relatively newer role which refers to people who work on strategic projects that generate pipeline. Think of Growth as a super ninja version of SDRs that focus on building projects across automated outbound, partnerships, paid traffic, marketing, inbound, and more.

  • Skills

    • Deep understanding of Revops, Systems thinker, Works well with new tech

  • Equity

    • Can vary based on cash comp

• Customer Success

  • Alternative Names | Post Sales Org

    • Post-Sales, Growth Strategist,

  • Customer success is a post sales role that involves helping with onboarding, getting customers to success, triaging bugs, and more.

  • Skills

    • Patience and tolerance,Reporting and solving customer bugs,Working with customers

  • Equity

    • Slightly more

• Engineering

• Design

Thanks for reading, you can reach me here:

• Linkedin

• Email

• Twitter

For those of you that follow for the restaurants / food content - checked out 3 places this weekend

1. Ye’s Apothecary - basement speakeasy with chinese food and cocktails on Orchard St. Food was pretty good and better than the drinks. Really cool vibe and not too loud which was nice.

2. Daddy’s - BYOB sidewalk italian spot in the WV. Cash Only. Place is a huge vibe and BYOB is super fun. Going to be a Saturday afternoon spot with a glass of Rose. They make some mean meatballs and a lemon parmesan linguine.

3. Mr Capri - New italian spot on 8th and 6th. Food is traditional italian and good but did not blow me away. Bar is open till 1AM which is huge (for a restaurant bar).

4. Manicure spot next to my apartment - The lads got foot massages and had drinks before going out on Saturday night. Was hilarious and made friends with the staff and other people in the venue while making our own drinks in some solo cups.

Talk soon.

GW