Market overviews
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Jun 28, 2023
Market: Nascent space with startup activity focused on Contract Management and Documentation use cases
Two points of context on Legal Tech are important to know before delving into AI Legal Tools:
It’s a Solar system - Large incumbents like Thomson Reuters, Lexis Nexis and Aderant are staples in the law firm middle/back office tech stack. Legal Tech ecosystem has evolved around these tools, focusing on specific value-adding use cases.
Not all AI is equal - AI tools built with NLP / ML at their core have been common in the space since as early as 2015, you would be challenged to find a startup in the space that doesn’t have “AI” in its description. For this analysis, we’ve focused on tools that are utilizing LLMs, which we believe are a major step up from NLP / ML and will unlock transformational value for legal use cases.
Contract management has been a growth area for Legal Tech over the last 5 years driven by a large TAM (as every company deals with contracts, and contract automation adds significant value) and the fact that you can sell products to corporates, thus avoiding a run-in with the large incumbents at law practices. Contract management tools offer contract drafting / editing, execution and contract analysis features. The application of LLMs has largely been on contact drafting - stepping up template-based drafts to auto-generated drafts from a prompt. Ironclad, LinkSquares and Evisort are series C+ startups with established customer bases that have invested in LLM-based functionality.
Legal Documentation tools serve primarily 2 purposes - a) finding information across a large set of documents, also called e-discovery (for e.g. finding relevant facts in a data dump submitted by a counter-party during litigation) and b) drafting documents like patent applications, legal communication etc. (some tools also provide contract drafting features in conjunction). LLM application has largely been on drafting as of now.
Legal assistants are an LLM-native concept that has emerged primarily over the last year. The concept is similar to ChatGPT and GitHub Copilot in creating a chat-based interface for a broad range of assistance. The number of tools exploring this use case are low currently, primarily because of the high accuracy requirement in legal advice. Harvey however has taken a novel approach to crack this market (more on it to follow). DoNotPay has created a new market for automated B2C legal advice, focusing on small disputes that lawyers wouldn't pick and customers would typically drop (e.g. rebates, parking tickets etc.). Tools like E-legal AI and AI Lawyer are also exploring legal chatbots for individual customers for simplifying legal jargon, asking basic legal questions etc., however the use is very nascent.
Legal research has been dominated by Westlaw (Thomson Reuters) and Lexis Nexis - every law practice more or less has access to these tools. Research thus hasn’t been a focus area for startups. Casetext is an exception - it was an early adopter of GPT-3 in 2020 to provide powerful semantic search and has been able to scale off of that.
Player archetypes: LLM applications have been pioneered by Established Legal Tech Startups, AI-Native Startups are nascent
In markets like Copywriting and Coding tools, a large set of AI-native startups emerged to pioneer LLM-based functionality. From this set emerged scaled startups that have attracted customer as well as investor interest (e.g., Jasper in Copywriting, Warp in Coding). In the AI Legal tools space, new-age startups haven’t emerged in earnest yet (Harvey is the exception); the drive to LLM-based functionality has come from established startups in the market. Some key factors driving this are -
Lack of a lower-end market - Across both Copywriting and Coding markets, new startups had a large set of individuals / small businesses to sell to (Freelancers, developers, marketers, etc.). Traction and product evolution in these segments allowed successful startups to then move up towards Mid-market and Enterprise. In the Legal space, the uses cases for individuals are scarce to non-existent. And law practices, including solo practitioners and small shops are well served by Legal Tech incumbents.
High bar for accuracy - Requirement for accuracy is higher in Legal than other markets, given the precise nature of legal process and potentially large consequences for small mistakes. The recent case of 2 lawyers being sanctioned for citing fake cases emerging from ChatGPT highlights the risk. Players with access to legal / customer data for training have a significant advantage - benefiting existing players.
Funding Landscape: Recent investment activity has largely targeted Establised Legal Tech Startups
Incumbents / established startups are better positioned to deploy LLM use cases than New startups given access to distribution and data. It is therefore not surprising that funding dollars have been directed towards relatively established players in the space.
Ironclad, LinkSquares, Evisort, Casetext, SpotDraft have all raised recent rounds with AI-based features a stated objective in the raise.
Harvey is an exception among new-age startups. An OpenAI-funded startup, it hit the headlines for it’s deal with Allen & Overy and has subsequently raised a $21M series A (Apr’23) led by Sequoia. Harvey’s approach has been unique - building bespoke AI models for their customers as collaborative projects vs taking a product-first approach. This has helped them get access and puts them in a great position to attain higher accuracy / relevancy in their offering, which in the future could lead to a killer product which can be transitioned to a product-first model. Perhaps their long waitlist can be an opportunity for other startups looking to capitalize.
Product Feedback: Gavel, Casetext, Evisort and SpotDraft are product leaders
Casetext’s product feedback underscores its strong traction and gives credence to Thomson Reuters’ rationale for acquiring it. Evisort is a well-established player and product leader in the contract management space.
Gavel (formerly Documate) is an emerging player in the legal documentation space with a unique approach - Gavel enables legal practices to create client facing workflows that help them deploy legal products along with their services (e.g. Gavel helped a small legal practice launch a rental lease agreement tool for its customers). The strong automation features, workflow integrations, and prompt customer support are key drivers of NPS.
SpotDraft is an emerging player in contract management. The product differentiation is based primarily on superior workflows (large set of integrations, central contract mgmt feature that tracks all changes, tasks related to contracts) and ease of setup.
Looking Forward
Killer LLM-based products are yet to emerge
While multiple startups have built LLM-based functionality, there isn’t proof of transformative value addition yet (similar to say the significant productivity benefits of AI Coding Tools). Who cracks the first killer LLM product is the most important question of 2023. Harvey, DoNotPay and Casetext would be our bets.
Growth-equity investor’s market
The most interesting startups in the space - Harvey, DoNotPay, Spellbook, SpotDraft have all raised at series A / B, and will be opportunities for series B / C investments in the near future. Casetext’s acquisition also illustrates potential for M&A; deals and buyouts in the space as startups scale.
Market will evolve through partnerships/acquisitions
Partnerships and M&A will be an important aspect of how the space develops. Thomson Reuters for example has pledged to invest $100 million in exploring generative AI for Legal. This has resulted in launch of new Westlaw Precision product, an investment in Spellbook, a partnership with Microsoft to integrate with 365 Copilot and now a $650M acquisition (admittedly more than the $100M they publicly committed) of Casetext. Other incumbents will undoubtedly be looking for M&A; opportunities as well.
AI Legal Tools is a market on the precipice, with clear potential for value but big challenges to achieve practical accuracy; we wait to see who can “raise the bar” and unlock this opportunity.
