Over the last few years, India’s ODR ecosystem has matured, moving from experimentation to adoption. It is now a multi-layered system comprising Horizontal ODR platforms, Vertical ODR (Sector-specific), Regulators, and Ecosystem Builders. Litigation in India is both expensive and time-consuming. A litigant spends close to Rs 1,039 on average, and disputes approximately take 1,445 days to resolve.

Despite improvements in speed and accessibility in the ODR ecosystem, India ranks poorly in contract enforcement at 163rd out of 190 countries according to the World Bank’s last Doing Business assessment in 2020. ODR not only addresses a justice gap but also reduces friction in commercial transactions by strengthening trust and improving the ease of doing business. Most ODR platforms continue to rely on post-facto enforcement. That raises a structural constraint in the current ODR stack. What would ODR look like if enforcement were built into the system?

This question points to the next frontier of ODR: blockchain-based, programmable dispute resolution. To understand how this ecosystem is structured today, it helps to map it across its core layer.

The State of India’s ODR Market

India’s ODR ecosystem is best understood as a collection of high-intensity pockets of dispute. ODR is not suitable for all disputes. It is suitable for disputes that satisfy three criteria:

• Disputes that are frequent

• Claims that are low to medium value

• Dispute resolution directly cuts costs and improves operations

Thus, ODR in India is suitable for high-volume, low-value disputes. India’s ODR ecosystem has attracted early-stage venture capital funding across platforms that include PreSolv360 (Series A $4.7 million), Sama (Undisclosed Seed round from Campus Fund), Jupitice Justice Technologies (Pre-Series A $4 million), Resolve Disputes Online (Seed Round), Webnyay (Undisclosed Seed Round), JustAct (Undisclosed Rounds), ADRes now (Undisclosed funding rounds), CADRE ODR (Pre-Series A $200k), Insurance Samadhan (Strategic funding round Rs 8.5 crores), and Credgenics (Series B $50 million).

Across the ecosystem, the capital is following the dispute density. For example, disputes in the insurance sector surged from 2.02 lakhs in FY23 to 2.57 lakhs in FY25. Specifically, complaints related to healthcare insurance rose by 41% year-on-year to over 1.37 lakhs in FY25. ODR platforms are concentrated in sectors such as Banking & Financial Services, Insurance, Debt Collection, and Consumer complaints. Furthermore, there is an opportunity to build a vertical ODR platform for property disputes, given that more than 66% of India’s civil cases involve property or land.

Early-stage venture capital has validated ODR as a category in India. Investors are funding ODR not merely for market size or unit economics, but because it unlocks a class of disputes that were previously uneconomical to resolve. Thus, ODR not only resolves disputes in the market but also expands it. ODR’s ability to expand markets and provide such access is often referred to as “Access to Justice.” This narrative needs a tweak. It should focus not on access to justice but on access to commercial justice, specifically for MSMEs and startups. India's formalized MSME base alone now approaches 9 crore registrations across Udyam and Udyam Assist.

ODR in India is not scaling through market adoption alone but is being institutionally accelerated. The strength of the Indian ODR stack lies in the mix of horizontal and vertical platforms, as well as public infrastructure. More often than not, these startups are credited alone for creating a burgeoning ecosystem. In the case of ODR, NITI Aayog was pivotal with its policy in 2021 titled “Designing the future of Dispute Resolution in India”. This policy plan highlighted the benefits, negatives, startups, and history of ODR in India, acting as a catalyst. SEBI launched Smart ODR to resolve disputes quickly between investors and market participants and is actively refining its ODR framework. Furthermore, the government launched the Samadhan portal to actively resolve MSME payment disputes and the Bima Bharosa portal for insurance complaints.

Agami has also played a crucial role by launching the ODR initiative in 2018, which has nurtured multiple ODR startups. They’ve been crucial in driving awareness and enterprise adoption as well. While ODR startups are concerned with execution (revenue generation), regulators are concerned with defining rules. Agami serves as the coordination layer between the two because changing how an enterprise resolves a dispute requires trust and behavioral change. Without this coordination layer, adoption would be fragmented, and enterprise buy-in would be harder to achieve.

Taken together, the Indian ODR ecosystem is not a single market but a layered system in which horizontal platforms resolve disputes across sectors, verticals drive adoption within specific sectors, and institutions create demand.

The Enforcement Gap: ODR’s structural constraint

The effectiveness of a dispute resolution system depends on two pillars: the speed of the decision and the speed of enforcement. Under Section 36 of the Arbitration & Conciliation Act, 1996 (“Act”), an arbitral award shall be enforced in the same manner as a court decree. In India, even if ODR can reduce the time to resolve a dispute to 45 days, enforcement of the award remains a central bottleneck. Without it, the award loses practical force.

Arbitration, often proposed as an alternative to litigation, illustrates this gap. Empirical evidence from studies and practitioners’ reports suggests that enforcing an arbitral award can take between 1 and 2 years on average, and may be extended if an application is filed under Section 34 or 37 of the Act. Historically, India has had poor contract enforcement, reflecting continued procedural inefficiencies. In Amit Chaurasia vs ICICI Bank, a single-judge bench of the Bombay High Court, comprising Somashekhar Sundaresan, upheld the validity of a pre-agreed ODR clause while passing an order. ODR clauses are therefore contractually enforceable, with courts increasingly recognizing technology-driven dispute resolution processes.

This introduces a paradox in dispute resolution. Despite Arbitration and ODR being designed to bypass traditional litigation, they ultimately rely on the same court machinery to enforce the decision. This undermines the ODR model, as ODR platforms optimize for swift dispute resolution. However, these gains become a nullity if enforcement remains time-consuming, legally complex, and strategically delayable.

For the class of disputes that ODR targets (high-volume, low-value claims), this becomes a constraint. If enforcing the decision requires additional litigation, costs, or fees, then enforcement can exceed the value of the claim itself. In such cases, resolution without enforcement is economically meaningless.

The enforcement constraint creates three structural limitations for ODR:

1. Post-facto dependence on courts - Ultimately, ODR outcomes rely on traditional execution proceedings to enforce the decision.

2. Strategic Non-Compliance - Losing parties can challenge awards under Section 34 and, where applicable, pursue appeals under Section 37, potentially delaying enforcement where a court grants a stay.

3. Weak Deterrence - Delayed enforcement reduces the credibility of outcomes and weakens incentives to comply voluntarily.

ODR today optimizes for resolution, not enforcement. It reduces the time to reach a decision, but not the certainty of realizing that decision. This distinction is crucial because until this gap is addressed, ODR remains an efficiency layer atop a constrained system rather than a fully self-contained alternative.

Blockchain-based ODR: Embedding Enforcement

The enforcement gap in ODR is not merely a procedural inefficiency. It is a design limitation. The present dispute resolution mechanism separates adjudication from enforcement. A decision is reached and then enforced through external mechanisms such as court proceedings. This sequencing creates the very problems identified earlier. Blockchain-based ODR proposes a different architecture in which enforcement is embedded in the transaction itself, provided the underlying asset or obligation can be automated through a smart contract.

Blockchain-based dispute resolution proposes a different architecture that rests on three pillars:

1. Verifiability and Tamper-Resistant Records - Blockchain is a distributed, public ledger. Transactions once entered cannot be changed. This makes it tamper-resistant and independently verifiable.

2. Decentralized Adjudication - Blockchain-based dispute resolution protocols can distribute adjudicatory functions among independent jurors. The precise mechanism varies by protocol. Kleros, for example, uses token-based economic incentives and randomly selected jurors to adjudicate disputes. This varies from platform to platform.

3. Smart Contracts (Enforcement) - This is the most important aspect of blockchain-based dispute resolution. Smart contracts are automated programs that self-execute when certain conditions are fulfilled. An action that involves transferring funds to another party can be performed without human intervention. Where the underlying asset or payment is held on-chain, a dispute outcome can trigger an automatic transfer, release of escrowed funds, or other contractual consequence without requiring a separate enforcement proceeding. This creates the possibility of moving enforcement from an ex-post legal process into the transaction architecture itself.

Blockchain does not necessarily eliminate enforcement but changes where it occurs. When the disputed asset or payment is programmable and is controlled by the smart contract, enforcement can occur on-chain. If the outcome involves off-chain assets such as bank accounts or property, traditional legal enforcement remains necessary.

Elements of such automated enforcement already exist, such as the escrow mechanism in e-commerce and automated chargebacks in payments. Blockchain extends this logic by enabling verifiable, tamper-resistant enforcement among parties that do not share a trusted intermediary.

Globally, startups have already experimented with this model. Kleros is a blockchain-based dispute resolution protocol that uses decentralized juries and token incentives to resolve online disputes. Smart contracts are used to enforce the decision. Aragon Court is a blockchain-based dispute resolution protocol that uses token-staked jurors and smart contracts to resolve disputes. LawBlocks AI is another company that offers services such as blockchain-based arbitration and mediation. There are platforms like Jur that seek to embed AI-powered blockchain contract automation into business teams’ workflows.

Although these models differ in architecture and maturity, they demonstrate a common principle: where the disputed asset or payment is held within a smart contract, the outcome of the dispute can directly trigger its transfer or release.

In India, Blockchain-based ODR remains nascent. As cross-border digital commerce expands, particularly across lending, payments, and e-commerce, the strongest opportunities are likely to emerge where the underlying asset or obligation, such as escrowed funds or tokenized assets, can be digitally controlled to automate enforcement. Despite its promise, blockchain-based dispute resolution is not without its limitations. Questions surrounding the irreversibility of smart contracts, jurisdiction, and legal recognition remain unanswered. More importantly, the enforceability of on-chain outcomes in off-chain legal systems depends on courts, particularly when crypto assets are at issue.

Blockchain-based ODR is suited to disputes in which the legal obligation can be enforced on-chain. Examples of this include digital payments, tokenized assets, e-commerce transactions, Domain Name Disputes, and Online Gaming disputes. A smart contract can execute an outcome, but it cannot independently know what happened in the real world for goods, bank accounts, or property.

Blockchain-based dispute resolution, therefore, does not eliminate courts; it moves enforcement closer to the transaction itself. ODR optimizes the process of reaching a resolution, while blockchain-based ODR can make the outcome executable where the underlying obligation is programmable. The shift is from post-facto enforcement to enforcement by design. For digitally native disputes, blockchain-based ODR represents the next layer of India’s ODR stack.

That’s everything for this month’s deep dive. If you’re building an ODR startup and would like to be added to the market map, you can reach out to me. Furthermore, funding rounds that are not hyperlinked are based on data from Traxcn.

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1. DPDPA Compliance Tools in India: The Complete Market Map (2026) - A map of 18+ startups building India’s DPDPA compliance ecosystem, from native compliance platforms to hybrid advisors and global privacy players.

2. The Venture Capitalist’s Trolley Problem - An exploration of how AI investment decisions can trade off productivity and abundance against employment, professional agency, and the apprenticeship models that create future experts. The piece asks a broader question: when VCs fund AI, are they merely backing companies or helping decide which version of the future of work gets built?

3. Three LegalTech Whitespace Plays in 2026: AI-Native Legal Services, Domain-Specific AI Agents & Cybersecurity Solutions - There are three opportunities I see for Legal AI in 2026: AI-Native Legal Services, Domain-Specific AI Agents, and Cybersecurity Solutions. This post makes the case for why now is the right time to build in these areas.

A fast-growing ecosystem has emerged to sell such tooling. The market map below captures the ecosystem as of mid-2026.

The DPDPA Compliance Ecosystem: The Native Stack, Hybrid Advisors, and Globals

The market map can be broken into 3 categories: The Native stack (India DPDPA-built compliance platforms), Hybrid Advisors (players combining AI tooling with hands-on consulting), and Globals (established privacy companies extending their GDPR/EU AI Act platforms into India and building India-specific capabilities). This market map skews heavily toward the native stack because that’s where most of the value in Legal AI company formation lies. If a startup or platform has been missed, reach out, and I’ll add them to the market map.

1. The Native Stack - DPDPA Shield, Redacto, IDfy (Privy), Leegality (Consentin), Perfios, Cross Identity (Vishwaas AI), HyperTrust, Neokred (Bluetic), Concur - Consent Manager, ConsentEZ, Consently, GoTrust, Complynz, Digio (CoTrust), ComplyDP, Nyai, Curated LegalTech, ClearConsent, and Jupitice Justice Technologies (Jupitice DPDP OS).

2. The Hybrid Advisors - DPDP Consultants and KavachOne (ConsentiQo platform).

3. The Globals - Seqrite, PrivacyEngine, Securiti, and Perforce

Consent Manager: The Software Tool vs. The Regulated Role Under DPDPA

Every vendor on the market map that sells tools calls their product consent manager. However, consent manager is a specific regulated status, and the two meanings describe fundamentally different things.

Under Section 2(g) of the DPDP Act, a consent manager is defined, and Part A of the first schedule and Rule 4 of the DPDP Rules, 2025, set out the conditions for registration as a consent manager. Consent managers under the Act and rules are licensed third-party intermediaries that allow individuals (data principals) to manage their consent across multiple data fiduciaries. Think of it the way RBI’s Account Aggregator works, where it is the one place to see your financial data consents across your banks, mutual funds, and insurers. A registered consent manager does the same for a user across their e-commerce platform, fintech app, and healthcare provider.

To become a consent manager, the applicant must have a minimum net worth of Rs 2 crore and apply from November 13, 2026. No entity possesses such registration as of yet. This distinction is important because both are different businesses with different moats and strategies. Building a software business is a tool for a single data fiduciary to manage consent on their platform. The second kind of business under the regulated status has stronger network effects because it has more fiduciaries, so startups building such tools for compliance will have to build out integrations with these regulated consent managers as well. The market map consists of players building a software tool for data fiduciaries to manage their consent.

The DPDPA Compliance Market Opportunity in India

India’s data protection market size was estimated at $6.3 billion in 2025 and is projected to grow to $28.3 billion by 2034, with a CAGR of 17.57%. Early demand for such tools is concentrated in regulation-heavy industries such as Government & Defense, BFSI, and Healthcare. This is why some of the startups building tools such as Perfios and IDfy were FinTech companies that developed adjacent products, including KYC verification, fraud prevention, and underwriting automation.

The opportunity in India lies not in building for large enterprises. However, it involves selling such compliance tools to startups in India. India has more than 8 crore MSMEs that are building startups across AI, Quantum Computing, SpaceTech, HealthTech, and FinTech. These enterprises usually need tools that ensure speed, quality, and efficiency. Data privacy regulations cannot become a bottleneck for enterprises at their nascent stage, as competition in these spaces is intense as well. Compliance should not prevent them from shipping their next product version.

The native-stack players have to figure out how to make DPDPA compliance feasible for the smaller players, and this cannot be priced as an Rs 40 Lakh enterprise contract. This distribution strategy provides companies with an advantage over those building for hybrid and global markets, because the market for larger enterprises is quite limited. The TAM for selling such a compliance product to MSMEs is bigger. To ensure a competitive advantage, these compliance startups will focus on speed, quality, and cost efficiency. If you’re building in this space or know someone who is, do reach out.

India's DPDPA Compliance Market Map — Mid-2026

The market map below provides a snapshot of India’s DPDPA Compliance market as of mid-2026.

Related Reads:

1. Mapping India’s LegalTech Ecosystem: Startups & Market Map (2026) - A weekly market map of interesting LegalTech startups in India building across segments like CLM, ODR, AI Copilots, Startup Legal & Infrastructure, and Estate Planning, etc.

2. Three LegalTech Whitespace Plays in 2026: AI-Native Legal Services, Domain-Specific AI Agents & Cybersecurity Solutions - There are three opportunities I see for Legal AI in 2026: AI-Native Legal Services, Domain-Specific AI Agents, and Cybersecurity Solutions. This post makes the case for why now is the right time to build in these areas.

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The trolley problem was a thought experiment formulated by English philosopher Philippa Foot and later popularized by Judith Jarvis Thomson in her paper. The trolley problem is a thought experiment in which a runaway trolley is heading toward five people on a track. You can pull a lever to divert the trolley onto another track, where it will kill one person instead. The question is whether it is justified to harm one person in order to save five others. The core question is whether it is justified to impose a smaller harm to achieve a greater benefit. Now, let’s contextualize this problem within venture capital, specifically focusing on LegalTech.

Imagine a venture capitalist deciding whether to invest in a LegalTech startup that can automate a large portion of legal work. If the startup is successful, legal services would become more accessible, and lawyers would focus on strategic work that requires exercising judgment, creativity, and agency. Yet this startup could eliminate entry-level associate jobs, as the tasks associates used to perform are now handled by its suite of tools. The question of AI’s impact on the future of work, specifically in BigLaw, continues to remain unanswered.

The VC does not directly determine outcomes in either case. Yet their decision shapes the kind of future being built. This is the venture capitalist’s trolley problem. The VC becomes the person standing by the trolley, and capital allocation becomes the act of choosing which track society’s technological future will follow.

How Venture Capital Funds the Future of AI

The general perception of venture capital investors is that they evaluate founders, analyze markets, and return capital to Limited Partners (LPs). VCs don’t just fund companies. They decide the kind of technological future that will be created for human civilization. Google, Facebook, Uber, and Instagram were all venture-backed startups. Though markets, regulators, consumers, and founders shape technological outcomes, VCs decide which ideas receive the necessary resources to compete in the first place.

VCs have to decide between multiple startups, alternative technologies, and different social outcomes. For every AI startup that VCs back, 100 could be rejected, or that capital could be used to fund mobility startups building EVs. Thus, every term sheet is more than just a financial instrument. It is a wager on one future and a rejection of countless others. Capital allocation is not only a financial choice but a moral choice in the context of trade-offs.

Unlike the classical trolley problem, the people affected by such decisions are rarely visible. The beneficiaries of a tool like Harvey could be multiple law firms that generate higher profits through efficiency gains. The costs of such technological change could be borne by workers in knowledge-intensive professions such as law, medicine, and finance. The trolley problem provides a useful framework for examining the broader societal trade-offs that VCs’ decisions entail.

AI and the Future of Work (Track #1): Productivity vs Employment

This first track concerns a trade-off between productivity and employment. AI startups are building tools across verticals such as customer support, legal, finance, coding, and healthcare. These startups usually automate a large portion of routine work. The benefits of such tools are cheaper costs, higher efficiency, and the opportunity to do work that requires strategic judgment. The downside of using such tools is fewer entry-level roles and disrupted career ladders.

The downsides are amplified in knowledge-intensive professions such as law, where associates develop judgment through drafting, research, and contract review. AI tools are disrupting the hierarchy model through which law firms generated revenue, in which associates billed hours for such tasks. The timelines for these tasks are becoming highly compressed and do not require a large number of associates.

This pattern is not unique to law. Many professions rely on routine work as a training ground for future experts. Junior analysts became investors by learning to model cap tables and DCFs. Junior engineers were promoted to senior engineers for their ability to solve problems through coding. These professions lose the very training ground that created future experts.

The deeper trade-off for the VC is whether it is acceptable to automate the very work through which professionals acquire expertise if doing so makes the profession cheaper, faster, and more efficient. From an investor’s perspective, the case for funding such companies is compelling. The question is whether those gains justify disrupting existing pathways through which future professionals are created.

Could AI Increase Abundance Without Reducing Human Agency? (Track #2): Abundance vs Human Agency

The second track concerns a trade-off between abundance and human agency. The most compelling promise of AI tools is that they can dramatically increase productivity across industries. Legal research, document review, financial modeling, and content creation can be performed with the assistance of such tools. If these trends continue, society will be able to create more wealth through better access to services.

The dominant narrative advocating for the adoption of such tools is that professionals would have more time to spend on tasks that require judgment, creativity, and intuition developed through years of experience in a domain. What this narrative fails to take into account is that these professions tend to derive satisfaction not only from the outcome but from the process of performing the work itself. Lawyers feel satisfied when they’ve found that one case law from the haystack. Engineers derive satisfaction when they solve a problem by writing code. Investors build conviction through research and diligence. As AI tools become more capable, they may assume responsibilities that were considered uniquely human.

The concern is not merely economic. A society that uses AI to perform most cognitive labor could be wealthier and more productive, but it would reduce professionals’ opportunities to exercise strategic thinking. Humans would find themselves as supervisors rather than being actively involved in the profession. Professional satisfaction arises from exercising judgment rather than merely producing outcomes. Agency is crucial, as the ability of humans to reason and deduce logically is what makes us unique.

The deeper trade-off for the VC is whether it is acceptable to sacrifice human agency in pursuit of greater abundance. The question is not whether AI can perform these tasks more efficiently. The question is whether a future in which humans do less meaningful work is necessarily a better future simply because it is more productive.

Why does the Venture Capitalist’s trolley problem break down?

The trolley problem provides a useful framework for examining the societal trade-offs embedded in venture capital decisions. Ultimately, the analogy remains imperfect. The classical trolley problem assumes that the decision-maker has complete information. The people on the tracks are visible, and there are only two known outcomes. The consequences of pulling the lever are known.

VCs operate under far greater uncertainty. When an investor funds an AI startup, they cannot be certain which industries will transform, who will benefit, or who will be harmed. The best example of this is that early-stage investors in social media startups such as Facebook could not have fully anticipated its impact on the mental health of teenage girls, promoting a false sense of beauty and body health. Likewise, investors backing AI startups today have no way of knowing which professions will be transformed, which new industries will emerge, or how society will adapt.

Thus, the challenge is that the beneficiaries and victims of technological progress are often invisible when an investment decision is made. Technological progress is hardly a zero-sum game. In LegalTech, new job roles such as legal engineers, product managers, and Legal Technologists are emerging.

The venture capitalist's trolley problem is therefore more complicated than its philosophical counterpart. Investors are not choosing between two known outcomes. They are making decisions about uncertain futures whose consequences may only become apparent years or decades later. In reality, the tracks themselves are constantly shifting.

Who gets to decide the future of work?

The trolley problem asks a very simple question: Should you pull the lever? The venture capitalist’s trolley problem asks who gets to decide which future is worth building? The benefits of AI are not distributed equally and may come at the expense of apprenticeship models and the decline in agency in professional identities.

To be clear, this is neither an argument against venture capital nor AI. VCs are not uniquely responsible for determining the future. However, venture capital does occupy a special position because it decides which ideas receive resources in the first place.

Every term sheet is therefore more than a financial instrument. It is a judgment about which futures deserve capital, attention, and belief. The defining question of the AI age may not be whether artificial intelligence should be built. It may be whether a small number of investors should possess such extraordinary influence over determining what the future looks like.

The trolley problem forces us to think about the morality of choosing between competing outcomes. The venture capitalist’s trolley problem forces us to confront something even more difficult: who gets to choose where the tracks are laid.

That’s everything for this article.

If you enjoyed reading this article, consider sharing it with a colleague in LegalTech.

A new generation of startups is building the technology infrastructure for the legal ecosystem, using artificial intelligence (AI) to automate lawyers’ workflows. The LegalTech ecosystem in India is evolving rapidly, with startups building across research, contract management, and dispute resolution. This post maps the key players, categories, and trends shaping the Indian legal tech landscape.

1. Contract Lifecycle Management (CLM) Software

2. Online Dispute Resolution (ODR) Platforms

3. AI Copilots for Lawyers

4. Litigation Finance Funds

5. Startup Legal & Compliance Infrastructure

6. RegTech Solutions

7. Legal Marketplaces

8. Estate Planning

9. Land & Property Intelligence

10. Court Infrastructure

11. AI-Native Legal Services

India’s legal services market, valued at $2.64 billion in 2026, presents a compelling case for LegalTech innovation, with more than 5 crore cases pending in the judiciary. There are 14,036 law firms in India, with more firms being launched by experienced partners breaking away from established firms.

This report maps several Indian LegalTech startups emerging across categories such as contract management, ODR, and compliance.

Methodology: How startups were selected

The startups featured in this landscape were selected based on a combination of product innovation, market traction, market opportunity, defensibility, and momentum. These 5 metrics were used to evaluate and select startups for this list.

1. Product Innovation - Startups were evaluated based on their ability to introduce technological solutions to complex legal workflows. Attention was paid to startups building AI Copilots, CLM software, ODR, RegTech solutions, Litigation Finance Funds, RegTech solutions, and Legal Marketplaces.

2. Market Traction - One key piece of evidence for a LegalTech tool is real-world adoption. Traction includes adoption by law firms, courts, institutions, corporate legal teams, and litigators. This allows us to consider factors other than headline funding.

3. Market Opportunity - India’s legal services market has multiple inefficiencies that can be addressed with LegalTech tools. Startups were evaluated based on market size, with companies addressing large addressable markets and recurring demand prioritized.

4. Defensibility - LegalTech startups derive long-term advantages from data and workflow integration. Sources of defensibility in this analysis include proprietary datasets, AI models trained on the firm’s data, and the level of integration with enterprise operations. Startups capable of building data moats through proprietary datasets such as firm data, contract drafts, and negotiation playbooks

5. Momentum - Finally, the list reflects startups that have shown strong momentum in the past 1-2 years. Momentum includes funding, product launches, and expansion into new verticals.

The LegalTech Stack in India

Contract Lifecycle Management (CLM) Software

1. SpotDraft: AI-driven CLM platform helping legal teams draft, review, and manage contracts faster across the entire contracting workflow.

2. SimpliContract: AI-native contract intelligence and CLM platform using AI agents to automate contract drafting, risk analysis, negotiation workflows, and lifecycle monitoring.

Online Dispute Resolution (ODR) Platforms

1. PreSolv360: Technology-driven ODR platform enabling businesses and individuals to resolve disputes through digital mediation, negotiation, and arbitration outside traditional courts.

2. Insurance Samadhan: Technology-driven ODR that helps policyholders challenge rejected or delayed insurance claims and resolve disputes with insurers through a digital grievance redressal workflow.

AI Copilots for Lawyers (Research, Drafting, and Argumentation)

1. jhana.ai: AI- Paralegal/Assistant platform that helps lawyers research case law, analyze documents, and generate legal drafts with reliable citations.

2. Indian Kanoon Prism: AI-powered legal research and learning platform built on the Indian Kanoon case law database, offering a suite of tools such as an argument generator, a research assistant, and document drafting.

3. Casemine: AI-powered legal copilot for lawyers featuring AMICUS AI Advanced, a conversational legal assistant that helps with case law research, document analysis, and drafting legal arguments using contextual precedent discovery.

Litigation Finance Funds

1. LegalPay: Litigation finance platform providing non-recourse funding for commercial disputes, arbitration, and insolvency cases using data-driven case evaluation and portfolio financing to turn legal claims into an alternative asset class.

2. FightRight: Litigation finance and LegalTech platform funding commercial and civil disputes using AI-driven case analytics to evaluate claims and help businesses pursue litigation without upfront legal costs.

Startup & Legal Compliance Infrastructure (Incorporation services for MSMEs, Companies, Sole Proprietorship, and One Person Companies)

1. Vakilsearch: Full-stack legal and compliance platform helping startups and SMEs manage incorporation, tax filings, IP registrations, and regulatory compliance through a digital interface and professional network.

2. RegisterKaro: Newer entrant in India’s startup legal infrastructure space, providing a platform for company incorporation, regulatory licenses, tax filings, and ongoing compliance, helping founders manage early-stage legal and regulatory requirements through a technology-enabled professional network.

RegTech Solutions

1. IDfy: RegTech and trust infrastructure platform offering identity verification, background checks, and fraud detection for enterprises, with Privy as its flagship product, enabling Banks and NBFCs to operationalize DPDPA compliance through automated consent and data privacy governance.

2. Redacto: Full-stack privacy compliance platform helping enterprises manage global data protection regulations such as GDPR, DPDPA, and other privacy frameworks through automated data discovery, governance, and compliance workflows.

Legal Marketplaces

1. LegalKart: A legal marketplace connecting individuals with lawyers across areas such as property, criminal, civil, arbitration, and IPR, with its core product, Challans, helping users resolve traffic violation cases through on-demand legal assistance.

2. Lawyered: Legal marketplace connecting individuals and businesses with lawyers for consultations and legal services, while specifically focusing on the mobility sector by helping logistics companies and truck drivers manage legal risks such as accidents, compliance issues, and challans.

Estate Planning

1. Yellow: Estate planning platform helping Indians and NRIs draft wills, set up trusts, and manage end-to-end succession through a guided digital workflow with post-demise executor support.

Land & Property Intelligence

1. The Land Bankers (TLB): AI-powered land intelligence platform helping investors, developers, individuals, and enterprises verify titles, assess ownership risks, and conduct land due diligence before acquisition.

Court Infrastructure

1. Adalat AI - An Indian nonprofit building AI-powered transcription, case-flow, and digital court infrastructure, now backed by Y Combinator.

AI-Native Legal Services

1. Lextron - AI-native legal services for startups and MSMEs, combining proprietary legal playbooks and AI-powered drafting with qualified lawyer review, fixed pricing, within 48-hours.

Key Trends in Indian LegalTech:

1. General-Purpose AI Assistants vs Specialized AI Assistants

AI Copilots are emerging across legal workflows. However, two distinct approaches are beginning to take shape.

The first category consists of General-Purpose AI Assistants that can research, draft, review, and analyze across multiple practice areas. India’s current generation of copilots falls into this category. We have yet to see the rise of Specialized AI assistants focused on legal workflows in areas such as M&A, IPR, and Labor Law.

These Specialized AI Assistants can be sold to corporate legal teams, law firms, and investment banks (for M&A). The rise of such assistants does not mean the disappearance of general-purpose tools. Rather, the ecosystem is going to evolve into a two-layer AI stack, in which general-purpose tools are used by lawyers, researchers, litigators, and arbitrators who work across a wide range of laws and disputes, and specialized tools are used by law firms and corporate legal teams that predominantly practice in a couple of areas.

2. Dispute Resolution is moving online and becoming specialized

India’s massive litigation backlog of more than 4 crore cases presents an opportunity for ODR to help resolve disputes swiftly. There are more than 90,000 cases pending at the Supreme Court of India alone.

The ODR sector is witnessing the emergence of verticalized dispute-resolution platforms, particularly in areas with high-volume complaints and where consumers face challenges navigating remedies. These areas are insurance, banking & finance, and MSME payment disputes.

3. Startup Legal Infrastructure is becoming a large market

India’s push towards entrepreneurship has led to the creation of over 654,460 startups, making it the 3rd-largest startup hub globally. This has created strong demand for platforms that simplify Company Incorporation, Tax & GST, and IPR filings.

As India moves closer to its ambition to become a $5 trillion economy, these startups could become the default option for small businesses and startups seeking legal services. Furthermore, these enterprises (MSMEs) have traditionally been a neglected vertical, as they survive on day-to-day cash flows and cannot spend as much as large enterprises. Thus, BigLaw has shied away from providing legal services to such enterprises.

The Indian LegalTech ecosystem is still in its early stages, but it is scaling rapidly as legal technology becomes more embedded across workflows. As AI continues to be embedded in legal workflows and dispute resolution, additional capabilities, such as memory systems and blockchain-based dispute resolution, could be introduced. The companies listed above are among the most interesting startups in these segments, transforming the practice of law.

4. AI-Native Legal Services are emerging in India

LegalTech is moving beyond tools that make lawyers more productive towards businesses that build legal service delivery around AI from the outset. These models can use AI to automate research, drafting, and other repetitive legal workflows while retaining lawyer oversight.

The opportunity is particularly relevant for startups and MSMEs, where traditional legal services can be expensive and slow. AI-native providers could offer faster turnaround, lower costs, and fixed-fee legal services, creating a new delivery model alongside traditional law firms. Lextron is one of the first companies to emerge from stealth, building AI-Native Legal Services from India for global markets, founded by Vatsal Gaur.

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In response, the market has been filled with a variety of tools for different use cases such as legal research, contract review, discovery, and compliance management. Despite the rapid adoption of AI tools in law, most innovation remains concentrated in productivity layers such as research, drafting, and document review. The deeper opportunity from a venture capital perspective lies in identifying “white spaces” across systems that will re-architect how legal work is priced, delivered, and scaled.

Looking towards 2026, I believe the most attractive venture opportunities lie in three white spaces: AI-native legal services firms, Domain-specific AI agents that absorb entire tasks, and Cybersecurity infrastructure built specifically for law firms. The time to build in these 3 spaces is now because buyers are converging on three priorities: lower costs with measurable outcomes, domain-specific agents with proven accuracy, and security architecture that can prevent data attacks.

1. AI-Native Legal Services Firms (The Next Generation of Law Firms)

The Indian legal services industry is built on the billable model. The billable model is a fundamental assumption that underpins the industry’s operations. Lawyers generate more revenue by working more hours. The pricing of such services is not based on the outcomes. Rather, it is based on the number of hours they work.

According to the Indian Business Law Journal’s billings survey, managing partners of law firms charge an average of $500 per hour (≈ Rs 44,950). These rates would be amplified in the case of elite full-service law firms such as SAM, CAM, Khaitan & Co, JSA Advocates and Solicitors, and AZB & Partners that control more than 25% of legal services. These BigLaw firms have operated for decades based on the billable model.

Here’s what an AI-Native Law Firm looks like:

a) AI is embedded into workflows such as drafting, research, contract review, and due diligence, not to augment lawyers but to reduce junior associate workload significantly. AI tools become a part of the core infrastructure. Instead of an army of junior associates to review contracts, an AI agent would be able to complete this task within minutes and not hours or days.

b) Pricing of services is not based on the number of hours billed. The price is based on speed, execution, and outcomes. For example, during an M&A transaction, lawyers charge for due diligence based on the number of hours spent. Due diligence would be fixed at a particular cost.

c) Lawyers in these firms would focus on high-value, strategic work that requires judgment, intuition, and experience, such as negotiation and structuring a transaction. Furthermore, these firms must implement a Human-in-the-Loop (HITL) model to ensure oversight of AI-generated work products, given the real risk of hallucinations. In a profession where errors are costly and precision is valued, human oversight is crucial.

d) AI-native law firms also have to build operating leverage. “Operating leverage” refers to growing revenue without exponentially growing headcount. In service businesses, revenue often grows with headcount. However, in an AI-native law firm, revenue has to grow without a linear increase in headcount. The graphs below show the growth of such firms.

(Graphs generated by Gemini)

e) AI-native law firms have to focus on reducing variance as well. LLM-generated drafts, redlines, etc., often produce variance. However, it is not the variance generated by LLMs that’s crucial. These firms have to build standard evaluation processes in order to flag such issues. Y Combinator’s playbook for building AI-native services holds that these standardized processes are crucial to building trust.

The most crucial differentiating factor between an AI-Native Law Firm and a Traditional Law Firm is the matter of “compounding intelligence”. In a traditional firm, the firm’s past knowledge often leaves when a partner leaves. However, in an AI-Native Law Firm, the tools will improve with each matter handled. If a partner leaves, knowledge remains within the firm.

BigLaw will struggle to pivot fully as its huge revenue streams are built on billable hours. Trilegal generated revenue of over Rs 1,000 crores in 2024 and was set to reach Rs 1,600 crores in 2025. These firms will suffer huge revenue losses by changing to a fixed-fee structure. However, they’ll repackage their services and retain billable hours by providing additional advisory services or by adopting a hybrid structure combining fixed-fee and billable components.

Billables have become part of lawyers’ professional identity. Removing billables is equal to killing the goose that laid the golden egg. Partner-level politics would cause conflicts, as their incentives to earn profits are closely tied to billables.

The niche for AI-Native Law Firms that I see is providing legal services for Startups and MSMEs. Traditionally, these enterprises were unable to access BigLaw legal services because they were small businesses that could not afford the high rates. BigLaw shied away from providing legal services as the margins were low.

India has nearly 5.93 crore MSMEs that account for nearly 50% of India’s exports. SMEs are projected to grow their share of legal services with a CAGR of 12.37% by 2030. MSMEs spend a whopping Rs 1.6 lakh crores annually on legal issues.

Building an AI-Native Law Firm for Startups and MSME would mean incurring a high Client Acquisition Cost (CAC) in the initial stages, but as time progresses, this cost gets spread over due to higher Client Lifetime Value (CLV), as small businesses that trust firms (loyalty) early on would require advisory services on multiple stages such as IP, Compliance strategy, Employment, and Fundraising. Furthermore, these law firms can build in areas such as ESG, International Arbitration, and Technology-risk advisory areas, where demand is outpacing supply.

2. Domain-Specific AI Agents

Perplexity’s latest study with Harvard shows the early-stage adoption of AI Agents, with 57% of Comet users already adopting agentic capabilities. These users are using such agents for productivity workflows and learning & research. Together, these two categories constitute 57% of agentic queries. These interactions with AI Agents usually occur in environments such as Google Docs (12%), Email, and LinkedIn.

Agentic AI adoption is driven by knowledge-intensive professionals in sectors such as legal, finance, and healthcare, constituting 70% of such adoption. In India, LegalTech tools are built for a variety of purposes. For example, jhana.ai’s paralegal is intended for use by lawyers across all practice areas. However, lawyers practice in different areas, including IPR, Data Privacy, Capital Markets, M&A, and Labor & Employment.

LegalTech startups should pick a practice area and build an Agentic AI product for that area alone. This will ensure superior answers, precision, and accuracy, as well as the LegalTech features lawyers are looking for. A great example of such a product is Perplexity Patents, which is positioned as an AI Agent to assist professionals with patent research.

Lawyers are looking for tools that are accurate to minimize human oversight and focus on strategic work that requires their experience. M&A transactions offer a promising domain to build an AI Agent. India recorded M&A transactions worth $26 billion, with volume driven by sectors such as technology & digital services, power, manufacturing, and infrastructure. Furthermore, with regulatory reforms such as clarification under competition law and tax guidance on ambiguities relating to capital gains, consolidation, and capital, this will continue to drive.

These agents would meet lawyers in Word and Outlook and assist them with drafting, summarization, case research, and contract review. The difference between using a chatbot and an agent is that an agent does not need continuous prompting, as it acts autonomously. AI Agents act like assistants that anticipate your needs. Other than M&A, startups can build agents for practice areas such as Capital Markets, Competition Law, Tax, and Technology Advisory.

Domain-Specific AI Agents enjoy a competitive advantage over generic LegalTech tools as they can be trained on domain-specific precedents, statutes, and templates. This ensures precision, accuracy, and fewer hallucinations. If you build an M&A agent, the customer base is not limited to law firms; it can also be sold to Corporate Development teams, Investment Banks, and Private Equity and Venture Capital Funds.

3. Cybersecurity Solutions for Law Firms

Law firms hold highly sensitive data with uneven security infrastructure. Firms often handle confidential data, such as Aadhaar numbers, litigation strategies, and financial records. Law firms face threats such as phishing, ransomware, financial extortion, and advanced persistent threats. The Indian legal services industry is built on two pillars: trust and reliability.

Cyber threats lead to direct revenue loss and the erosion of client trust. The American Bar Association (ABA) reported that up to 42% of law firms with 100 employees or more have experienced a data breach. There is no India-specific data on law firms facing cyber threats.

A great example of such a cyberattack is the leak of the Panama Papers from the law firm Mossack Fonseca, which resulted in the release of 11.5 million documents. As a result of the adverse publicity the firm received due to the leak, it closed in 2018. Thus, cyber threats can be very costly if not addressed properly.

India has 14,036 law firms as of October 2025, up 5.11% from 2023. The number of law firms is expected to increase as partners from top law firms leave to launch their own ventures. The market size is expanding.

A cybersecurity solution for law firms would ideally have the following features:

1. Document vaults to store case files, communications, and records

2. AI-driven anomaly detection trained specifically to understand law firm threat patterns

3. Compliance tracking with cybersecurity rules such as the IT Act and the DPDP rules

4. Multi-factor authentication (MFA) to ensure security

5. Integration with CLM systems and AI tools

6. Continuous monitoring + Alerts

Cybersecurity solutions are a non-discretionary spend for law firms because of compliance with the DPDP rules. Furthermore, as law firms employ AI to automate workloads, clients demand transparency about how their data is stored. A ransomware attack on sensitive data could halt a law firm’s operations, affecting revenue and goodwill. Firms that have taken out a cyber insurance policy must implement these solutions. Cybersecurity is becoming non-negotiable rather than an optional IT expense.

These cybersecurity solutions have to be specifically built for law firms because of differing patterns across enterprise segments. The solution must be trained on law firm-specific threat patterns to ensure accurate threat detection. For example, a phishing email targeted at a partner to reveal a client’s litigation strategy.

Conclusion

The next wave of LegalTech in India will not be shaped by marginal productivity tools. It will be shaped firm that re-architect processes around how legal work is produced, namely: AI-Native Law Firms that charge based on outcomes, domain-specific agents that absorb workflows, and cybersecurity infrastructure that protects sensitive information.

The most valuable LegalTech companies will move from bolting AI into law practices to a software-orchestrated operating model. These startups are not tools but are building infrastructure that silently powers drafting, diligence, and contract review. If you’re building in this space, the time is now. Pick an area, solve end-to-end workflows, or build security-grade infrastructure for law firms. These LegalTech whitespace opportunities highlight how AI is reshaping legal services, AI Agents, and Cybersecurity solutions, creating a new category of venture-backable legal startups.

Sources (In no particular order): PIB, Bar & Bench, Ethan Batraski’s substack, Mordor Intelligence, Lexis Nexis, Financial Times, Alex Baker’s Linkedin post, Perplexity and HBS’s paper “The Adoption and Usage of AI Agents: Early Evidence from Perplexity”, India Briefing, Forbes, Perimeter, Indian Express and Rentech Digital, Deloitte report “The future of legal work”, Financial Express, Arctic Wolf, Enterprise IT World and The amikusqriae.

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The process of due diligence involves a detailed evaluation of vendor agreements, NDAs, employment contracts and identifying pending lawsuits involving the target company. Traditionally, due diligence is an expensive and time-consuming process costing 0.5%-2% of the M&A transaction.

For example, in an M&A transaction where the target company is valued at $400 million, due diligence costs would amount to anywhere from $2M-$8M. AI-driven tools such as Kira are enabling lawyers to review such contracts within minutes and perform predictive analysis on areas of potential liabilities. Furthermore, these tools are reducing the costs and margin of error associated with M&A transactions.

This article analyzes how AI-driven due diligence tools are transforming M&A workflows and redefining the lawyer’s role from document reviewer to a strategic deal advisor.

Where Deals Are Won Or Lost

Due Diligence forms the backbone of a M&A transaction. Due Diligence not only involves evaluating potential legal liabilities and compliance but also understanding post-merger risks such as cultural and technology risks. Lawyers have to conduct a thorough evaluation of contracts, IP, HR policies and compliance with tax and data privacy laws.

As Warren Buffett said “A sales pitch gives you the price, while due diligence gives you the value.” In a survey conducted by McKinsey, respondents admitted that due diligence was inadequate in more than 40% of their deals. In Nirma Industries vs SEBI the court held that the investing company has to conduct proper due diligence on a target company before investing.

For example, in the Daimler Benz and Chrysler group merger the German automotive manufacturer merged with the Chrysler group for $36 billion. The merger occurred in 1998. However, the value of the Chrysler group went down to $7.4 billion. Experts have cited that the merger failed due to inadequate evaluation of regulatory changes, cultural integration, employee expectations and oversight of financial position.

The Broken Diligence Process in M&A

Before we delve into the bottlenecks of due diligence we have to understand how legal due diligence is different from financial or operational diligence. Legal due diligence is different from financial or commercial due diligence because of reviewing unstructured documents in natural language.

Financial due diligence involves reviewing financial position, operational discipline and bank records of the target company. However, legal due diligence involves reviewing a wide range of documents from which clauses have to be extracted and deviations have to be analyzed to mitigate potential liabilities.

Lawyers primarily face four bottlenecks in legal due diligence:

1. Volume of Contracts - During the course of an M&A transaction, lawyers have to review a wide range of contracts such as licensing agreements, vendor agreements, employment contracts, NDAs. The length of these contracts could vary from a couple to thousands of pages.

2. Time Constraints - M&A transactions usually take 3-6 months to close. The volume of contracts to be reviewed places significant pressure on lawyers. This means they cannot review each and every contract. Lawyers often handpick contracts that could lead to missing out on issues that could affect the transaction. In a study conducted LawGeex, lawyers were able to evaluate five NDA contracts in 92 minutes.

3. Margin of Error - Lawyers have a higher margin of error in comparison to AI tools due to volume of contracts to be reviewed. Furthermore, lawyers not only have to review contracts as part of the process but also evaluate the compliance of the target company with laws such as Income Tax, 1961, DPDP Act and policies such as HR policies to mitigate post-merger integration risks. In the study conducted by LawGeex, the software had a higher accuracy of 94% in comparison to the accuracy of lawyers which stood at 85% on average.

4. Cost of Due Diligence - Due Diligence is an expensive process that costs 0.5%-2% of the M&A transaction. From the previous example given an M&A transaction valuing the target company at $400 million would lead to costs of $2M-$8M. Due Diligence incurs high-costs due to the process being labor-intensive requiring an army of junior associates.

AI-enabled Due Diligence: Shift in Scale, Speed and Accuracy

AI-driven tools are transforming due diligence workflows in three ways:

1. Document Review - Lawyers are able to review contracts and documents within minutes and not months. Due Diligence usually takes 3-6 months of which the majority of time and labor goes to document review. AI-driven workflows are able to cut this time down to less than a month. A holistic and comprehensive review of the target company can be done. An army of junior associates are not required to review thousands of contracts. AI-driven tools cut down document review time by 90% while still requiring human validation.

2. Checklist Automation - Due Diligence involves maintaining checklists to ensure that documents are reviewed and compliance checklists are maintained. Law firms usually maintain such checklists in paper-based workflows that are unstructured and hard to keep track of. AI-driven due diligence tools bring such checklists digitally into a single platform and thus making it easier to monitor such checklists.

3. Risk Mitigation - Junior associates might not be able to identify and extract clauses due to vast number of documents and checklists that have to be maintained. AI-driven due diligence tools are able to identify and extract problematic clauses in contracts and documents. Furthermore, these tools could employ predictive analysis based on the disputes (history) of the target company to understand where potential liabilities might arise.

Clifford Chance’s Experience: Leveraging Kira Systems in M&A Due Diligence

Clifford Chance, one of the world’s leading law firms adopted Kira Systems in July 2016 as part of its strategy to deliver effective and efficient services to clients. Kira was used as part of its M&A practice in a transaction that involved reviewing more than 4,000 documents across multiple jurisdictions. Although, lawyers had to verify the outputs of Kira, the firm saved 20% of time that would have been spent on this exercise.

AI-driven due diligence tools help augment document review and risk mitigation. Furthermore, these tools can be tailored to the firm’s expertise to suit its proprietary workflows. This case study is an exemplary example of how AI-driven due diligence tools augment legal judgement allowing senior partners to focus on deal structuring and negotiation.

The Case for Sector-Specific, Predictive Legal Diligence

LegalTech tools in the market focus on horizontal use cases such as contract review and e-discovery. These tools perform retrospective analysis and not predictive analysis to understand where liabilities might arise in the future.

There is a clear gap for sector-specific, predictive legal analytical tools that can analyze legal and regulatory risks post-merger, model sector-specific risk patterns and inform deal pricing, structure and strategy. This is because legal risk, deal structure and risk patterns are sector-dependent. Horizontal tools lack the domain-expertise and risk models required. Furthermore, due diligence tools should not only automate document review but assist in building insight based on these reviews.

For example:

• FinTech: AML, RBI/SEBI compliance, data localization, KYC failures

• DeepTech: IP ownership, open-source exposure, R&D dependency risk

• LegalTech: Data privacy, privilege risks, regulatory uncertainty

• SpaceTech: Export controls, sovereign licensing, cross-border liability

A single due diligence tool cannot assess and assist in mitigating such risks. Sector-specific predictive analysis allows forward-looking insights that might look at failure post-acquisition and quantify the risk with the deal structure and strategy. In practice, this allows lawyers to mitigate issues rather than just listing them down.

The buyers of such a LegalTech product would not consist of law firms but Private Equity and Venture Capital Funds, In-house teams, Strategy teams, Corporate Development teams and Alternative Legal Service Providers (ALSPs). The time for building such a product is right now due to M&A deal volume increasing, deal timelines compressing and legal data (contracts) being digitized.

From Document Review to Deal Strategy

The value proposition of a lawyer shifts from a document reviewer to an active deal strategist. Lawyers are able to engage in high-value, strategic work such as negotiation, issue mitigation and risk modelling. Lawyers are legal modelers that have to quantify the risks that could arise post-merger such as lawsuits, antitrust issues and cultural risks such as employee satisfaction and compensation.

AI-driven due diligence tools augment the role of the lawyers allowing them to focus on deal strategy to help build insight for their clients. This is something AI-driven tools will never be able to replace because negotiation and deal strategy are tasks that are built by lawyers through the course of their career and experience in M&A transactions. AI-driven due diligence tools will only be able to automate the manual and routine tasks such as document review, checklist automation and legal modelling (predicting potential areas of risk).

Junior associates do not have to spend hours to read contracts and maintain paper-based checklists to evaluate companies. These associates will be given hands-on experience in deal strategy, mitigating risks (through prediction) and legal modelling. Junior associates will transition from doing manual review to deal strategy, risk assessment and technology-enabled legal analysis thus requiring legal acumen and technical proficiency.

Conclusion

As M&A activity accelerates and transactions grow more complex, traditional due diligence processes are unable to keep pace. The cost, time, and error-prone nature of legacy workflows not only inflate transaction expenses but materially contribute to deal failure and post-merger value erosion.

AI-driven due diligence tools are reshaping this by addressing the core limitations of scale, speed and accuracy. These tools are automating document review and standardizing checklist maintenance. Lawyers are shifting from mechanical review to higher-value, strategic work such as negotiation, issue mitigation and risk modelling. The result of this is not a replacement of legal professionals but a redefinition of legal value.

The next frontier lies in sector-specific, predictive legal intelligence. These tools do not merely identify risks, but model them, quantify their impact, and inform strategic decision-making. This represents a significant opportunity not only for LegalTech founders and investors, but also for law firms, in-house teams, and deal professionals willing to rethink how legal services are delivered.

Law firms, in-house teams, and dealmakers must move beyond checklist-based diligence and adopt AI-enabled, insight-driven workflows. These tools should not merely enable document review alone but build insight using predictive analysis. Lawyers who develop expertise in these tools and in risk modelling will define the future of M&A practice.

Sources: Thomson Reuters, TechCrunch, Anirvan Sen “A Comprehensive Guide to M&A Costs: How Much Should You Spend?”, Dealroom, LegalEase Solutions, Raconteur, National Jurist, Clifford Chance “Artificial Intelligence and the Future for Legal Services”, LiveLaw, Lexology and Artificial Lawyer. Images were created by Gemini.

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If you enjoyed reading this article, consider sharing it with a colleague in LegalTech.

Related Reads:

1. The Venture Capitalist’s Trolley Problem -https://harshithviswanath.substack.com/p/the-venture-capitalists-trolley-problem

2. The Three LegalTech Whitespace in 2026 -https://harshithviswanath.substack.com/p/three-legaltech-whitespace-plays

3. Perplexity Patents: AI Patent Tool Transforming Patent Research -

   https://harshithviswanath.substack.com/p/perplexity-launches-perplexity-patents

In India, the cost of non-compliance is rising faster than the cost of doing business. This is due to the growing complexity of regulations and changing nature of laws such as DPDP Act, Income Tax Act, ESG regulations and RBI norms.

Banks and NBFCs in India were fined by the RBI for Rs 33 crores in 2024-2025 for non-compliance. This is just the tip of the iceberg. MNCs, Startups and Banks require compliance for a variety of purposes such as KYC, Fraud Detection, GST compliance and Regulatory change monitoring.

The next set of bets in LegalTech will be in RegTech startups that create business value by driving efficient compliance. Compliance is no longer a back-office function. It’s becoming a part of core infrastructure.

An enterprise that neglects compliance will drown in paper-based workflows crippling their business and draining resources for compliance. This denies opportunities for value creation by preventing investments in product development, business development, partnerships and marketing.

What is RegTech?

RegTech (Regulatory Technology) refers to software that automates, monitors and operationalizes compliance obligation. RegTech is different from other tools that lawyers use for legal research or contract review as RegTech is not used for review purposes. Rather, RegTech solutions are used to operationalize compliance.

RegTech solutions are used to for complying with a variety of regulations such as DPDP Act, GDPR Act, KYC, AI regulations, Anti-Money Laundering and Vendor Due Diligence. RegTech solutions usually possess features like generating regulatory reports, identifying irregularities by pattern-matching and predictive risk assessment (Identify areas of non-compliant behavior).

Startups that are building in this space are as follows:

Source: Unleash

Market Size

India’s RegTech market was valued at $516.4 million in 2024 and is expected to grow to $2,183.9 million by 2033 with a CAGR of 17.9%.

Source: IMARC Group

Why is RegTech exploding right now?

1. Regulatory load is compounding - India is seeing a massive wave of innovation and the rise of new technologies such as AI, Blockchain and Quantum Computing all of which require regulations. These regulations are required in areas such as data privacy, KYC, AML and Fair practices code etc. India has more laws than it did 10 years ago.

2. Non-Compliance destroys enterprise value - The cost of non-compliance destroys enterprise value. Enterprise have to pay fines and this could even lead to shutdowns. This would cripple a Seed to Series A startup in India. There have been instances where RBI has fined huge amounts for non-compliance. For example, Union Bank of India was fined Rs 63.6 lakhs for breaching regulatory norms.

3. Economies of Scale - In-house counsels usually spend the bulk of their time in compliance. Usually they hire specialists (Law Firms), to ensure compliance with a variety of laws. However, with the help of RegTech solutions enterprises and companies can reduce their costs as they can handle most of the compliance in-house due to reduced burden. For example, Kompile enabled a fintech client to automate compliance for over 5,000 monthly loan applications, cutting compliance costs by 20% while improving borrower onboarding speed.

4. Contract Negotiation - Corporate legal departments often juggle multiple contracts at the same time. Compliance overload often leads to outsourcing such functions to law firms and outside counsel. However, companies are starting to recognize the real-time business impact of such tasks. Thus, they want their own legal departments who understand the needs and types of contracts to be signed to negotiate their contracts.

5. Reduced margin of error - Before the evolution RegTech solutions in-house counsels often had to create checklists in paper-based workflows and constantly update them because of the dynamic regulatory environment. This would often lead to missing out on something or the other. However, with RegTech solutions such errors are reduced as regulatory changes are automatically updated with all checklists automatically created.

India’s massive whitespace for investment opportunities

India presents a massive opportunity for RegTech startups because of a growing customer base including Startups (FinTech), Banks, NBFCs, Financial Institutions and Companies.

India mainly presents an opportunity in the following areas:

1. Data Privacy: With the growth of AI startups and India’s new DPDP Act privacy tooling and ensuring compliance is of paramount importance to legal departments. The importance becomes amplified in light of the increasing number of cross-border data transfers.

2. RBI/SEBI Governance: India is seeing a massive increase in Fintech startups. These startups have to comply with a variety of regulations like Account Aggregator (AA) Framework, Lending Disclosures, PIT regulations and Listing Disclosures and Disclosure Requirements (LODR) to name a few.

3. Digital Public Infrastructure: The government is expanding it’s DPI through measures such as Government Marketplaces, FASTag, Ayushman Bharat Health Mission, UPI and Aadhar-linked health schemes. These require sector-specific solutions. Furthermore, the India government is going to expand India’s DPI in the upcoming years. Building government specific solutions for vendor contract compliance, consent management and disclosure requirements is a huge opportunity.

4. ESG Mandates: Listed companies that are building in manufacturing industries such as cement, steel, semiconductors and renewable energy need to comply with ESG mandates. Listed companies have to as the cost of non-compliance with such mandates could mean a dip in the stock prices.

Conclusion

Compliance is becoming a part of core infrastructure that drives operations and could open up opportunities for companies. Startups that build specialized tools for regulations and then spin-off into other domains of law will succeed. Data Privacy compliance is poised to be the market leader with explosive growth in the upcoming years.

Lawyers are not going to be replaced by RegTech solutions. They’re going to be able to operationalize compliance and focus on high-value tasks such as negotiation, vendor due diligence and structuring a contract. AI is coming for compliance and companies that do not evolve will be left behind. RegTech is the next billion-dollar bet and the opportunity has arrived to start building.

In one of the defining moments for LegalTech, Clio has acquired vLex for $1 billion. This marks the biggest M&A transaction in LegalTech. The deal was financed by a Series G raise of $500 million by Clio valuing it at $5 billion. Clio has cemented its status as a fast evolving category leader in the nascent LegalTech space.

LegalTech is a sector that has often been held back because of the lack of capital in comparison to its peers such as Fintech and Healthtech. This is a watershed moment for LegalTech and a signal that it is entering an era of scale.

The Players and Deal

Clio was founded in 2008 and is one the global leaders in LegalTech. Clio offers a diverse range of products for lawyers such as legal billing, case management, document automation and legal research etc.

vLex was founded in 2000 and is mainly known for its flagship product called “Vincent”. Vincent is a legal assistant that automates research, streamlines litigation and transaction processes and provides strategic legal insights such as understanding the judge’s litigation history and reasoning patterns.

Deal Math

The M&A transaction was funded by a Series G raise of $500 million from investors such as New Enterprise Associates, TCV, Goldman Sachs Asset Management, Sixth Street Growth and JMI Equity. The financing of the transaction included a significant debt component of $350 million from Blackstone and Blue Owl Capital. These two private credit funds have a history of supporting high-growth and innovative SaaS companies.

Oakley Capital, one of the majority shareholders in vLex chose to take stock as part of Clio signaling investor confidence and long-term sustainability of Clio.

Why does the deal make strategic sense?

Clio’s acquisition of vLex marks the launch of the Intelligence Legal Work Platform that transforms Clio from a system of record to a dynamic system that leverages vLex’s database that has billions of documents from different jurisdictions. The acquisition is a vertical integration play.

Clio is able to make use of vLex’s client base expanding its reach from small and mid-sized firms to the world’s largest law firms and in-house teams. Previously, Clio acquired ShareDo which was adaptive work management platform for lawyers. This evolved into Clio Operate that has expanded into US and developed a dominant presence in the UK.

Similar to this, vLex will be rebranded as Clio for Enterprise and the unique moat that Clio has achieved through this transaction the legal intelligence database that vLex has developed. The database is vast and containing document ranging different jurisdictions. This will help Clio help achieve it’s goal of global expansion.

Conclusion

The acquisition of vLex marks an inflection point in the LegalTech where the industry is starting to see consolidation and capital inflows that are sustaining billion-dollar acquisitions and valuations. Furthermore, this shows that LegalTech startups have a long-gestation period and the right investment could deliver superior returns (vLex).

Clio wants to build the next layer of legal infrastructure by leveraging its existing operating system and the legal intelligence database that vLex has built over the years. The future of the law is not only in AI models and software but in building databases that can be leveraged as well.

The Problem that Perplexity is trying to solve

There are multiple databases for patent search such as Google Patents, USPTO Patent Search and Lens. Traditionally, patent search has relied on the usage of extensive key words and phrases making it inaccessible for the general populace. These platforms require professional training and expertise. This coupled with their costs limited it to specialists. Perplexity Patents wants to democratize access to IP knowledge for the general populace.

How does Perplexity Patents work and why is it better?

Perplexity Patents is an AI research agent. The AI Agents breaks down the complex query into small and precise tasks. Then the agent goes to the specialized patent index where it searches for related documents and technical information. Then agent crafts responses and weaves the requisite documents into a single response. Instead of doing 10 searches manually, the same can be achieved with 1 search in Perplexity Patents.

The advantage is that you do not need to ask it specific questions. You can ask the agent vague and open-ended questions such as the “Key AI Agent patents in 2025” or even “ Blockchain patents in 2025”. The competitive advantage it possesses is that if you ask it a question relating to a specific product such as fitness watches, it will provide results for adjacent patents such as wearables and fitness bands.

This minimizes the number of searches that have to be conducted creating efficiency and saving time. Perplexity Patents draws not only from patent literature where innovations might be cited but also academic literature, public software repositories and other relevant sources making the search comprehensive and thorough.

Why is Patent Research hot right now?

• Patent Research helps founders, innovators and technologists identify whether they’re inventions are actually new and not present in the market already. If your invention already exists then thousands of dollars will be wasted on the patent application. If there’s something similar in the market, then your product can be modified.

• Patent Research helps prevent infringement with competitors and helps identify whitespaces for innovation by showing what rivals are doing, where they’re building and their future products in the pipeline.

Patent research is hot right now with the number of DeepTech startups that are using technologies such as AI, Blockchain and Quantum Computing. Owning proprietary technology has never been more important in the world of startups. Perplexity Patents is not only a LegalTech product but also a product for engineers, innovators and researchers and business leaders.

BigTech companies are starting to penetrate the LegalTech industry. OpenAI is already using a internally developed Contract Review tool. These companies already have a massive market share and could evolve into Full-Stack AI companies that provide tools for a wide range of areas such as Law, Accounting, Finance, Auditing and Client Relations.

LegalTech companies such as Harvey, Legora and Eudia will face stiff competition from these tech giants. These tech giants play on a different scale altogether. Perplexity Patents was given for free to 30 million users sending a signal that they’re here to stay and want to capture this market.

The LegalTech wars have begun and this time, BigTech wants in.

The authors alleged that Anthropic had downloaded millions of copyrighted books from databases such as Library Genesis and Pirate Library Mirror. These books were used in training Anthropic’s Generative AI Assistant Claude. The settlement tells us an important detail that the datasets acquired by AI companies have to be acquired through channels like licensing.

This settlement means three things for early-stage LegalTech companies building in India:

a) Data ≠ Free Fuel

Copyright law does not care whether you’re a scrappy two-person team or a trillion dollar giant. Improperly acquired data from pirated databases could lead to potential liabilities of up to millions if not billions of dollars. Many founders treat data in the internet as public. They should read into the fine print to assess whether the usage of such data would fall under “fair use”.

b) Funding and Due Diligence

VCs will become more sophisticated about data governance. Data Governance becomes a risk that could collapse an entire startup. Due Diligence would involve questions like:

1) How the datasets were obtained?

2) Are there proofs such as documentation and licenses for fair use?

3) Are there contractual indemnities from data vendors?

c) Data Sourcing becomes a moat

Data Sourcing will become a key differentiator in whether startups survive or not. To source data LegalTech companies could adopt strategies such as licensing datasets which would involve budgeting for licensing agreements with authors and creators. Partnerships with publishers can be established to gain access to a databases. Data Sourcing is crucial as one of the reasons AI chatbots like ChatGPT, Claude and Gemini are effective is because of the vast amounts of data it is trained on.

Early-stage compliance looks expensive but it is better than a Series A startup getting bankrupted by a potential settlement worth billions of dollars. However, this case holds no precedential value as it was settled.

Sources: New York Times, Hindu, Reuters, BBC and Business Insider (Image)

Third Party Litigation Funding (TPLF) refers to a third-party not connected to the dispute funding one of the parties to a dispute in exchange for a share in the proceeds of the settlement/award/damages. It’s basically when someone else is footing the bill for you and reaping in the benefits once the dispute has been won.

There are more than 40,000 civil cases filed every day across India. Often many of these litigations are discontinued due to the high-cost and time courts take. Litigation costs in India have risen to 31% of the claim value and the average Indian would incur Rs 1,039 per day per case.

TPLF is emerging as an asset class in India due to rising costs of litigation. However, India lacks specific regulations towards litigation finance. As opposed to countries such as USA and UK which already have established markets for TPLF India is still at a nascent stage. In this post we’ll breakdown TPLF and regulations surrounding along with an analysis of the market opportunity.

Market Opportunity for TPLF

Globally, the litigation funding market was valued at $10.9 billion in 2018 and is expected to double by 2027 at a value of $22.4 billion at a CAGR of 8.3%. The market is currently valued at $17.5 billion in 2025. The Indian litigation funding market is expected to grow to more than $57 billion by 2035.

The key drivers for the growth of the litigation finance market are:

1. Rising Litigation costs: Fighting a case in the Indian judiciary is a long, expensive and time-consuming endeavor. Litigation costs have risen to 31% of the claim value. This coupled with abysmal judge-population ratio in India (1 judge for 58,800 people) has lead to inefficiencies leading to backlog of more than 40 million cases.

2. Burgeoning startup ecosystem: India is currently at the golden era of innovation wherein technologies such AI, Quantum Computing and Blockchain are on the rise. In the coming years, we’re going to see a rise in technology disputes in areas such as copyrights for AI-generated content, Data Privacy and Digital Contracts.

3. Attractive return: Litigation finance offers non-market, non-correlated returns that is often attractive to alternate institutional investors such as Family Offices, Fund-of-Fund(FoF) and High-Net worth individuals(HNIs).

4. Risk Mitigation: Litigation finance offers lower risk compared to other alternative assets such as Private Equity (PE), Venture Capital (VC) and Hedge Funds. This lower risk is partly due to the integration of AI-models that are capable of evaluating cases through their merits. According to Nitin Jain, the founder of FightRight the litigation finance is structured in a manner such that in a portfolio of 8-10 cases even if you get 4-5 cases right everyone still makes money.

Though the market potential of such startups is very high due to emerging market demand the legal waters still remain untested and uncertain.

Regulatory Status of Litigation finance in India:

As of yet, there is no law that regulates litigation finance in India. However, Indian law does not prohibit TPLF. In Ram Coomar Coondo vs Chunder Canto Mookerjee (1876) Privy council held that TPLF agreements are valid on the grounds that they promote access to justice. However, it becomes invalid if such funding is for abetting unrighteous and frivolous law suits against public policy.

To better understand the regulations that surround litigation finance we must understand the doctrines from which it was initially prohibited. The doctrine of maintenance and champerty fundamental to the principle of common law did not allow TPLF due to fear in the rise of vexatious or frivolous suits. Historically, this is why litigation finance was illegal.

However, in modern times countries such as USA, UK and Australia have abolished this doctrine and have a thriving litigation finance ecosystem. This is because precedence was given to the doctrine of access to justice. TPLF provided financing for suits/cases where the plaintiff did not have financial backing to fund such a case.

In Bar Council of India (BCI) vs A.K Balaji (2018) the Supreme Court (SC) observed that a conjoint reading of the rules 18, 20, 21 and 22 of the Standards of Professional Conduct and Etiquette of the BCI rules prohibited an advocate from funding their clients for a case. However, third-parties (non-lawyers) can finance cases.

From a conjoint reading of the Privy Council’s decision and A.K Balaji it can be assumed that TPLF agreements are valid provided they are not against public policy and satisfy the requirements of a valid contract under the Indian Contract Act, 1872.

Though TPLF could potentially be argued as a valid contract in court there’s a need for a specialized act. When compared to a global jurisdiction such as US with a robust litigation finance ecosystem India is nowhere close. The courts in USA have generally upheld the validity of TPLF agreements provided they do not contravene public policy or they’re immoral.

However, the US TPLF market is not without its challenges. There’s still no transparency with regard to TPLF agreements. This would involve answering questions like whether or not the TPLF agreements have to be disclosed to the opposing parties and counsels. The arguments put forth by both side with regards to this issue seem to be meritorious.

Singapore solves this issue by having a balanced model. In 2017, Singapore amended its Civil Law Act to expressly permit international arbitration. The Singaporean courts have taken a proactive stance to address the ethical issues surrounding TPLF agreements. The rules require the party getting funding to disclose it and ensure that funders have no control over the legal process.

Emerging opportunities for startups

There are mainly 2 emerging market opportunities for startups in the TPLF sector. They’re mainly Arbitration Disputes and Insolvency proceedings.

Arbitration is attracting funding because commercial disputes in India could take anywhere from 5-15 years to be resolved. With more than 24 million cases pending in the system arbitration offers a quicker and efficient method of dispute resolution. Arbitration is attractive for investment because:

a) Large pool of cases: In a survey conducted by PwC in 2013, 87% of the companies surveyed had experienced domestic dispute resolution. When these companies were asked what sort of mechanisms they use to resolve disputes 95% reported using arbitration as a standalone mechanism.

Companies prefer arbitration over litigation because of 3 reasons:

1. Litigation is expensive and time-consuming. Disputes that get resolved by arbitration have a time horizon of 1-3 years as opposed to courts which could take 10-15 years to pass a judgement.

   

   Arbitration offers a shot at higher returns with a lesser time horizon.

2. Litigation fails to maintain the confidentiality of the proceedings damaging the reputation of the companies involved in the dispute.

Insolvency Proceedings are emerging as a huge market opportunity. TPLF funds the Corporate Insolvency Resolution Process (CIRP) of companies and helps them liquidate and recover as much money as possible and gets a share of the proceeds. 7,567 companies were brought into administration till March, as per the Insolvency and Bankruptcy Board of India.

There are very few startups that are building in this space in India. The well-known startups are LegalPay, LitiCap, Fightright and LegalFund. Globally, the market is dominated by established players such as Parabellum Capital, Burford Capital LLC, Bentham Capital, Apex Litigation finance and Omni Bridgeway.

This market will remain domestically dominated for quite a long time period due to the level of understanding required of the Indian judiciary’s inefficiencies, court politics and regional dynamics. This can only be done by a person who has years of litigation experience as a lawyer to pattern-match the winning cases.

Conclusion

Litigation finance is emerging as an asset class due to attractive returns and the possibility of non-market, non-correlated returns. Though, right now the Indian litigation finance market in India is only occupied by 4-5 startups, with growing attention from regulatory bodies such IRDAI, RBI etc we could see more startups flourishing. The regulations will determine the future of India’s litigation finance ecosystem.

Sources: Economic Times, Law.Asia, PwC’s study on Arbitration in 2013, Insolvency Tracker, Bar & Bench, SCC Online, Business Today, RegStreet Law Advisors and Majumdar & Partners and Matter Suite.

One of the promising ideas was Full-Stack AI Companies. LLMs are automating a bulk of the legal work such as drafting, case research, due diligence, documentation and compliance.

This presents 2 opportunities:

a) You could build your own AI Agents and sell them to law firms and lawyers

(OR)

b) You could start a law firm and staff it with existing agents

If you picked B then that is building a Full-Stack AI Company. This could be done for industries which are dominated by slow-moving incumbents. A Full-Stack law firm would compete with existing law firms and could potentially run out traditional law firms from business.

Full-Stack AI Law Firms would be able to run more efficiently than traditional law firms due to leaner teams. It would be cost-effective due to reductions associated with human labor.

Just like how in VC we’re slowly seeing the rise of Solo-GP Funds the next big law firm will not have thousands of lawyers. It could possibly be a single-partner firm.

As we all know, the legal industry is one that is slow-moving and is plagued by procedural inefficiencies such as case research, paperwork, documentation, compliance and rote drafting.

What are AI Agents?

Before we delve into what a Full-Stack Law Firm looks like, it’s important to understand what AI Agents are and the technology behind them.

An AI Agent is a software or program that is able to act autonomously on behalf of people helping optimize workflow. I’m sure we all know about Jarvis from Iron Man who was Tony’s personal assistant (AI Agent). Now imagine a law firm run by Jarvis clones in charge of different departments such as Public Policy, Client Relations, Finance, M&A, PE and VC. That’s the entire pitch.

The tech for AI Agents isn’t quite there yet, but we’re getting there.

From this we can see that an AI Agent would replace the need for hiring people as it can make independent decisions and perform complex tasks.

Where India stands

In India, if we take a look at the state of LegalTech right now all the Indian Law Firms are slowly adopting AI. Recently, Shardul Amarchand Mangaldas (SAM) announced a partnership with leading Generative AI platform Harvey AI. SAM plans to use the renowned platform for contract review and drafting, predictive analysis, legal research and due diligence. IndusLaw had announced a partnership with Jurisphere and previously even LegitQuest which is an AI-driven tool. Khaitan & Co has developed it’s own Generative AI solution called KAI that can be utilized within the firm ensuring confidentiality.

How would Full-Stack AI Firm operate?

A Full-Stack AI Law Firm would consist of different AI Agents in charge of different departments or an AI Agent consisting of sub-agents. It would have more agents if it was a full-service law firm as they work across diverse areas. If the firm was a boutique law firm specializing in some areas then it would require fewer AI Agents. Examples would be M&A Agent, Capital Markets Agent, Client Relations Agent and HR Agent etc. These different agents would be overseen by a team of maximum 10-15 members.

In short, law firms are going to have teams the size of VC Firms. Those who have a high level of expertise in an area coupled with the skills to oversee such agents would be preferred for hiring. These law firms would be able to compete with the big law firms in India such as SAM, CAM and Khaitan & Co. With the entry of foreign law firms in India we could see new firms which would be able to compete with foreign law firms due to their high-efficiency and lean teams. Your lawyer would be a robot!

Challenges associated with Full-Stack AI Law Firms

1. Bias - AI learns from historic data and if the data contains biases then it amplify biases. Law Firms that rely on these AI Agents may suffer from discriminatory outcomes hurting their reputation and outcomes. They may end up prioritizing efficiency over fairness and privacy. E.g. When these AI Agents assess the risk of startups from non-English speaking countries during due diligence these startups might get red-flagged. This is because the model could have been trained on Western-Centric approaches, past due-diligence reports, region etc.

2. Data Privacy - Law Firms have to maintain a high level of confidentiality due to Attorney-Client privilege. When a law firm is completely digital it has to be ensured that none of the confidential documents are leaked. The firm’s proprietary data should be protected. Their non-dynamic structure makes them more prone to cyber attacks.

3. Lack of Dynamic Learning - Corporate Law Firms have a dynamic work environment as business regulations and law are subject to change. For example if we take M&A, then the Competition Commission of India is constantly updating and upgrading the regulations. AI Agents are trained on static information. The AI Agents have to be updated continuously.

The Next 10 Years

A Full-Stack Law Firm is not something that we might see very soon in the Indian Startup Ecosystem. It will take another good 10 years before we see such law firms in India as even the development of AI Agents is at a very nascent stage. The Legal Industry is slow-moving and is adopting AI to increase it’s efficiency and cut down costs. A Full-Stack Law Firm is very well in the cards and could in fact be a game changer for legal industry. Would you trust you Jarvis with your next lawsuit? I wouldn’t bet against it.

Sources: Y Combinator, Bar and Bench, IBM, Google Cloud, Zapier, BCG, ETLegalWorld and Lexis Nexis. Images are generated by Gemini.

But what exactly do we mean by a LegalTech startup? Let's take the case of a rising startup called "LegalKart".

LegalKart is an online legal consultation platform where anyone from anywhere can access a lawyer on demand. These startups meet two pressing needs in the legal services markets:
1) Higher Efficiency and Productivity
2) Access to speedier justice for the common man

What is a LegalTech startup?

LegalTech startups are companies that leverage to technology to build solutions that can automate legal workflows to improve efficiency, access to justice and reduce costs.

Market Sizing and Growth of LegalTech

India’s LegalTech market is currently valued at $385 million and has close to 835 startups making it second only to USA. Globally, the LegalTech industry is currently valued at $29.60 billion and is estimated to reach $68.04 billion by 2034 growing at a Compound Annual Growth Rate (CAGR) of 8.7%.

The Indian market broadly consists of 4 types of startups in the LegalTech industry:

1. Legal Services Delivery and ODR

2. Legal Marketplaces

3. Legal Research and Analytics

4. Document Automation, Compliance Management and Virtual Assistants

The market map below maps all the key LegalTech Companies in the Indian market. [This is not a comprehensive list as there are a whopping 835 LegalTech startups in India. These are just some of the known or key players in the industry.]

Why LegalTech matters for India?
Now there are mainly 4 reasons why there is a rise of LegalTech startups in India:

1)The insane court backlog: India has a humongous backlog of 4.5 million cases with 40,000 civil cases being filed everyday across the nation. The next question that arises is how exactly do these LegalTech startups help solve judicial inefficiencies and backlogs.

Let's take the example of Presolv360, an Online Dispute Resolution(ODR) startup that provides an online platform for mediating disputes quickly. They've reduced the time for solving a case from 13 years to 3 months along with bringing down the litigating cost to $20. Presolv360 is not only making dispute resolution efficient, but is delivering justice in a completely new form.

2) Abysmal judge-population ratio: The abysmally low judge-population ratio can be linked back to the 1st reason. India has 1 judge for every 58,800 as opposed to the optimum requirement of 50 judges per 1 million people. There is a shortage of judges and according to data judges at the Supreme Court and High Court spend only 5-6 minutes per case.

3) Covid-19 Pandemic: During the Covid-19 pandemic the Indian judiciary and lawyers were forced to adopt technology in the form of E-courts and court hearings happening in online platforms like Google Meet and Zoom. Covid-19 played a pivotal role in introducing the first wave of technology adoption in India.

Where does the opportunity for innovation lie in the industry?

There are mainly 4 opportunities that I see for innovation in the LegalTech industry:

1. Specialized LLM Models: Generative AI is largely the technology that is being integrated into the Legal market. The companies that develop models for specific areas of law such as Capital markets, Private Equity and Competition Law would be able to create accurate models that would automate case research for lawyers because the information needed to train such models would be limited to that specific area.

2. Conducting Due Diligence: Due Diligence is an integral part of every transaction that a lawyer advises on. Over 85 M&A deals, valued over $75 million, were conducted in 2020. Due Diligence essentially involves pouring through tons of documents before a lawyer could flag potential financial or compliance issues. With the advent of AI it could sift through these documents within a couple of minutes and flag issues such as financial and legal risks, compliance issues and analyze competitor data along with market trends.

   

3. Vernacular Languages: While the official language of the Supreme Court (SC) and High Court (HC) continue to be English, the district courts allow for the usage of vernacular languages as per the directive of the State government. As a case progresses to the higher tiered courts the documents and evidence have to be translated into English. Large LLMs in vernacular languages are being developed by startups such as Sarvam AI. Sarvam AI has recently launched its text-to-speech AI model in 11 languages.

   

4. Smart Contracts: Blockchain technology is in a very nascent stage in India with respect to the LegalTech industry. Smart contracts are digital agreements that set certain conditions through the language of code. The smart contract once set up cannot be changed and does not require any signature. The advantages of a contract are that it reduces confusion and eliminates the middlemen. However, the problem with respect to India is that it does not have a regulatory framework for smart contracts. The law is not clear on the rights and duties of the parties to a smart contract.

   

Challenges to LegalTech adoption

Counter Factual#1- The Legal industry has been traditionally slow to adopt technology as lawyers are resistant to let go of the paper filled style of work. Lawyers have to be convinced that these developments in technology would make their work more efficient and productive. In a profession like law wherein the experienced professionals are usually old, would require more convincing due to limited technological familiarity.

Counter Factual#2 - The lawyers are concerned about their jobs being taken away. Previously, Generative AI was used only for back-end functions but with the advent of more startups they are being used for more front-end functions like contract automation and due diligence. This causes panic among lawyers that they’re jobs will be replaced by AI. These concerns have to be alleviated by showing that the technology will not replace lawyers but will be integrated into their work ecosystem to produce results efficiently and productively. Lawyers have to be convinced as they are the core target market.

Counter Factual#3 - These startups face threats of data privacy as they handle highly sensitive documents (confidential) such as contracts, personal identity details, financial transactions, case files and evidence. This might lead to reputational damage, legal liability, violation of attorney-client privilege and identity theft/fraud.

India’s LegalTech revolution is no longer a distant reality but is rapidly unfolding. Gone are the days where lawyers had to sit in their offices and read through tons of pages to flag issues. These startups are not only making legal work more efficient and productive, but providing access to legal resources to common man through ODR platforms and virtual assistants. India’s future isn’t just in the courtrooms. It’s in cloud, code, and collaboration.

Sources: Inc42, IIMA Ventures Insights, IndiaToday, The Hindu, Law.Asia, International Legal Technology Association, YourStory, Law.com, Economic Times, Grand View Research, Lawyered’s official website, Vakilsearch’s official website, Artificial Lawyer, Organiser, VIA Mediation Centre, Technical.ly, INDIAai, Shrimpy Academy.

VC investments in the AI space have seen huge surges in the past few years. Early-stage AI startups cumulatively raised close to $560 million in funding. Startups such as Krutrim, Sarvam.AI and Mad Street Den have raised significant funding from VC investors. Krutrim was valued at $1 billion in its last funding round. This showcases the potential of AI startups to provide huge exits to investors.

This blog will explore the key drivers, trends and challenges faced by AI startups in India.

Source: https://techeela.com/technology/a-spotlight-on-top-ten-generative-ai-startups/

Why are VC investors betting big in India?

VC investors are betting big on Indian AI startups, mainly because the usage of AI in industries is expanding. These areas include content, gaming, astrology, health, education and law.

VC investors are extremely interested in India’s AI industry due to the realization among founders and experts that general foundational models cannot constitute a successful business model for a startup. Furthermore, developing a model is expensive and requires a lot of investment.

In India, this is why there is a rise of Vertical AI startups that focus on solving problems within specific industries such as legal, healthcare and construction, rather than building general-purpose AI models. SpotDraft (Legal Field) and Track3D (Construction) are examples of Vertical AI startups. Vertical AI startups are gaining traction even in competitive markets due to their ability to laser-focus on solving specific problems.

While the growing demand for AI solutions is a major factor in driving VC interest, government policies towards AI startups further enhance their attractiveness. The Indian Government increased the budget of the AI Mission by allocating Rs 10,372 crores for building computing infrastructure through public-private partnerships. India’s new Draft Deeptech Policy 2023 aims to accelerate technological growth and global competitiveness.

These policies not only offer startup funding but offer opportunities for research, access to unified data sets, and promotion of AI applications for startups tackling socio-economic challenges. These benefits make the launching of AI startups easier and offer easier access to resources for building an AI startup. Investor confidence is boosted.

In addition to all of this, India's burgeoning talent pool is one of the reasons for VC investors. Companies and the Government are launching initiatives for continuous upskilling and it is crucial to ensure this. Otherwise, there would be disequilibrium in the demand-supply of talent which is crucial to ensure innovative startups.

Challenges faced in scaling AI startups

1) Data Privacy Regulations

AI startups in India have to comply with the regulations set forth in the Digital Personal Data Protection (DPDP), 2023. This is a huge challenge for such startups because access to high-quality data is vital for training models. Using publicly available data could conflict with existing copyright and the DPDP bill. Inaccuracies and bias in the GenAI applications involving hiring, marketing, and insurance claims pose critical risks. Questions relating to whether the data fiduciary or the startups developing the model are crucial. AI Regulations are still in the nascent stage and, therefore, have to be navigated by startups through legal counsel specializing in AI.

2) Limited Talent Pool

AI being a highly specialized and niche field, the number of professionals is limited. Sourcing the right people with the correct skills is crucial for building an AI startup. The technical expertise required for building a model is high. Initiatives like the government’s YUVAi (Youth for Unnati and Vikas with AI program) and companies like Tata Consultancy Services, Wipro, and HCLTech continuously upskilling employees would help increase the talent pool.

3) Bias in Training sets

The data sets used for training AI models must be inclusive, as India’s diverse population requires attention to prevent discriminatory output in AI systems. Discriminatory output by AI systems could exacerbate the existing prejudices and biases in society. This problem is mainly faced by smaller startups, which often lack the resources to promote fairness and responsibility. This can be solved by using diverse datasets, including a wide range of scenarios and demographic groups. The models should be tested on a regular basis, and “Bias tests” can be conducted. These tests can be utilized to update the models and reflect the latest changes in society.

Conclusion:

AI startups in India are rapidly evolving due to a combination of factors mentioned above. They are poised to revolutionize India’s startup ecosystem, and the growth is driven by favorable government policies, increasing talent pool, and a shift towards Vertical AI startups.

While the potential for investment and innovation is immense AI startups must navigate challenges such as data privacy, stagnant talent pool and bias in training sets to ensure their success. Despite this, India’s AI startups continue to grow through upskilling initiatives and inclusive data sets creating a ripe environment for innovation.

The future of AI startups in India is bright but the success of these startups will depend on them innovating responsibly and navigating these challenges successfully.

Introduction

India’s startup ecosystem has seen a significant rise in venture capital funding. Startups usually secure funding with a variety of options, such as Convertible Notes and Priced Rounds. iSAFE (India Simple Agreement for Future Equity) notes are gaining popularity as a method of early-stage fundraising.

iSAFE were initially created from the Y Combinator SAFE agreements. Their primary advantage lies in helping to sidestep valuation which is a significant hurdle for securing early-stage funding. However, iSAFE contracts remain a double-edged sword in India providing much needed flexibility to startups but at the same time lack clarity due to its untested nature in India’s venture capital and startup landscape.

This blog explores iSAFE contracts, their benefits, and the legal hurdles that they carry.

What is an iSAFE note?

iSAFE notes is a convertible instrument in which a Venture Capital investor makes a cash investment which can be converted into equity at a later date. It was introduced by 100XVC in India which is an early-stage investment firm. iSAFEs take the form of Compulsorily Convertible Preference Shares (CCPS) and are convertible into equity shares on a specified date.

India’s startup ecosystem spans a range of sectors, including Healthtech, Deeptech, Consumer, and Fintech. These startups are usually in the pre-revenue stages, being in an ideation stage or having only developed a Minimum Viable Product (MVP). Estimating a valuation for such a startup is an arduous task. This is where the iSAFEs help with the valuation aspect.

Before delving into how iSAFEs solve the valuation problem, we need to understand two major concepts, which are a priced and unpriced round. A priced round is a round in which the startup has a valuation. An unpriced round is a round in which the startup’s valuation is not determined. In an iSAFE, the valuation of the startup is deferred to a future event. This event could be anything ranging from a Merger, Acquisition or IPO and is known as the triggering event. iSAFEs are contracts that typically contain 5-6 pages. This helps startups secure quicker funding and reduces the need for expensive legal counsel.

Legality of iSAFEs

While there are no specific laws governing convertibles such as iSAFEs, they are indirectly regulated under Section 32 of the Indian Contract Act, 1872, and Sections 42, 55, and 62 of the Companies Act, 2013 read along with Companies (Share Capital and Debentures) and Companies (Prospectus and Allotment of Securities) Rules, 2014.

SAFE notes are considered contingent contracts under Indian law, governed by Sections 31 and 32 of the Indian Contract Act of 1872. A contingent contract is one where performance depends on the occurrence of an uncertain event. The key characteristics of contingent contracts, which SAFE notes fulfill, are:

1. Conditional Nature: Conversion of SAFE to equity is contingent on uncertain future events, such as a liquidity event or funding round.

2. Collateral Contingency: The event triggering the conversion is collateral to the main investment contract.

3. Independent of Promisor’s Will: The triggering events are pre-defined and not subject to the will of any party.

4. Condition Precedent: Performance (conversion to equity) occurs only after the specified condition is fulfilled.

Provisions governing iSAFE notes [Companies Act,2013]

1) Section 42 – Section 42 outlines the process for private placement of securities, which aligns with the structure of iSAFE notes offered to a select group of accredited investors.

2) Section 55 – This section governs the issuance of preference shares and specifies that CCPS must be converted into equity shares within a stipulated time or upon a triggering event. Triggering events could be IPO, Merger, Acquisition or Dissolution.

3) Section 62 – This section deals with the further issue of shares, such as rights issues and employee stock options, and allows the company to issue shares to existing shareholders or third parties. Upon conversion of iSAFE notes into equity, this section is triggered. The pricing of shares during conversion must adhere to the terms of iSAFE notes.

iSAFE notes are gaining traction in India, though not widely utilized. 100XVC invested close to $2.3 million as part of its 10^th cohort through iSAFE notes. It has invested Rs 1.25 Crores in startups such as Arthum, Bepure, Buckmint, Cutbox, Dailybee and Datavio.

For the accounting of iSAFE notes, there are no clear standards or guidelines prescribed by the Institute of Chartered Accountants of India (ICAI). iSAFE notes are listed as “Preference Share Capital” because legally they are issued as CCPS. In a balance sheet iSAFE notes are typically classified under “Shareholder’s Funds”.

The tax treatment of iSAFE notes is not very clear. Section 47 (xb) of the Income Tax Act, 1961 can be examined. This provision clearly states that the conversion of preference shares into equity shares is not a transfer and, hence, not taxable.

Advantages to startup and investors

1) Benefits to Startups:

a) iSAFE notes do not accrue interest since its not debt

b) iSAFE notes are less expensive since experts need not be hired for drafting complex Shareholder Agreements (SHA)

c) iSAFE notes are simple documents that consist of 5-6 pages eliminating the need for 50-page contracts

d) The transaction can conclude in a span of 2-3 days ensuring timely funding

2) Benefits to Investors:

a) iSAFE notes get preference over equity shareholders in case the startup fails

b) Investors close deals within 2-3 days

c) There is no dilution in the cap table until a liquidity event or the next priced round

Risks associated with iSAFE notes

The concept of iSAFE notes is fairly new to startups, investors and corporate regulators. There are certain risks associated with enforceability due to its hybrid nature, combining features of both equity and debt instruments. The Companies Act, 2013 and its associated rules do not explicitly prohibit the issuance of iSAFE notes. iSAFE notes started gaining traction in 2019 and there have been no such publicly document disputes or notices regarding iSAFE notes.

When disputes arise there is question about the nature of the iSAFE notes because it is not fully an equity instrument since iSAFE notes convert into equity shares only on the happening of certain events or from 3 years of the issue of the note whichever comes earlier. It is neither a debt instrument since it does not accrue interest and does not appear as “debt” in the startup’s cap table. The nature of iSAFE notes is one of the important challenges that have to be addressed to promote investor confidence.

iSAFEs are marketed as “standard” and “industry accepted” template. However, labelling iSAFEs as standard contracts could have catastrophic consequences for the startup and venture capital industry. iSAFE notes have to be customized according to the investment, sector and risk appetite of the investor. The template of the iSAFE could potentially serve as a guideline but the specific conditions of the contract have to be customized. This could lead to startups and investors overlooking the need for legal counsel. Disputes may arise,e prolonging the investment process.

Though iSAFE notes provide startups with flexible funding, they create valuation risks. In an iSAFE note, a valuation cap or discount, whichever is higher, is usually provided as an incentive to the Venture Capital fund to make the investment. A valuation cap could potentially act as an anti-dilution provision. This is because in subsequent rounds, such as Series A, the investors may get more equity shares if the valuation is less than the valuation cap.

iSAFE notes also mitigate some of the risks that were present with the Y combinator SAFEs. The original SAFEs are based on a triggering event and if this triggering event does not happen then the SAFEs do not convert into equity. However, in iSAFE notes the likelihood of this happening is close to impossible since the shares issued are in the form of CCPS.

Global Perspective

The Securities and Exchange Commission (SEC) of the United States of America (USA) issued a notice titled “Be cautious of SAFEs in Crowdfunding,” listing the risks associated with SAFEs that we have discussed above. One of the major risks highlighted in the notice was the fact that if the triggering event does not occur, the investor does not get any equity. However, this risk is solved in the Indian context since they are issued in the form of CCPS. The notice suggests that instead of a SAFE a convertible note which is a SAFE like security in some aspects could be issued. This is because it offers retail investors protection associated with being a debt instrument. However, convertible notes accrue interest which might not be beneficial for a startup that is in its early-stage requiring investment in Research and Development (R&D), Intellectual Property (IP), Operations and Team Building.

Conclusion

iSAFE notes are an innovative approach towards early-stage funding and are tailored towards India’s startup ecosystem. However, the untested nature of iSAFE notes and the lack of clear regulations continue to limit its use in India.

The challenges associated with iSAFE notes have to be addressed proactively. The Venture Capital Fund and startups should customize the terms of the agreement, which would reflect the needs of the transaction. The investor and startups should negotiate a fair valuation cap that would ensure the founder’s ownership is not diluted to an extreme level. Furthermore, ICAI should issue guidelines for the accounting of iSAFE notes.

While iSAFE notes are no panacea, they address crucial challenges in startup funding. Since iSAFE notes are relatively new in India, the abovementioned challenges should be addressed through engagement with relevant stakeholders. Clearer regulations will also help promote their adoption.