AI applications & software · Industry Index 9 (rank #139) · $11.0B — Growth round, 2026-03editorial estimate
ⓘ Data freshness
price: no data index: as of 2026-10-29 fundamentals: no data No structured SEC filings
AI platform for law firms and in-house legal teams. Runs custom agents for M&A, due diligence, contract drafting and document review, and publishes the BigLaw Bench evaluation framework for legal LLM work.
Industry exposure 1.0 (Pure-play AI application for legal work; the product is AI-generated legal work product end to end.)
Signal
signal: no data
Not scored (private). Harvey is a private company, so its directional signal is not published.
Relationship network
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Microsoft — partner
OpenAI — supplier
RELX plc — competitor
Key relationships
Microsoft — partner · Harvey runs on Azure OpenAI Service infrastructure and fine-tunes models jointly with Microsoft to customise them per customer. source: microsoft.com
OpenAI — supplier · Harvey post-trains and fine-tunes OpenAI models — o1-preview, o1-mini and GPT-series via Azure OpenAI Service — for its base product and for individual customers. source: microsoft.com
RELX plc — competitor · LexisNexis, RELX's legal division, ships Lexis+ with Protege into the same law-firm buyer as Harvey; the article contrasts LexisNexis's £959M H1 2026 revenue against Harvey's $300M ARR. source: nonbillable.co.uk
Unusual options activity needs a US-listed instrument; this company has none.
Not scored (private)
We don’t publish a directional call for private companies.
No moat profile yet. The durable competitive advantage — moat, leadership, and pricing power — is curated for flagship names first; Harvey isn't covered yet. Nothing here is fabricated in its absence.
Signal contribution
The moat prior — a subjective, non-backtested editorial input — only nudges the long (~6-month) horizon, and it isn't scoring Harvey yet (directional scores are withheld for private companies). Nothing here is fabricated in its absence.
Owns the customer relationship, the workflow and — unusually — the evaluation methodology the whole vertical is measured against, but adapts third-party frontier models rather than owning one, and the proprietary data is largely the client's. Four-fifths of the thesis, missing the model layer.
Sells directly into 1,300+ organisations across 60+ countries including the majority of the AmLaw 100, 500+ in-house teams and named customers NBCUniversal, HSBC and DLA Piper International, with legal engineering teams embedded at the customer. source: harvey.ai
Models are augmented with proprietary data for specific legal tasks, but the worked example is a custom tax model trained on a professional-services firm's own internal tax data by that firm's in-house experts — the corpus belongs to the client, not the platform. source: microsoft.com
Built BigLaw Bench, an evaluation framework designed by its own legal research team of practising attorneys, scoring tasks derived from lawyer time entries against custom rubrics on two metrics — Answer Score (share of a lawyer-quality work product completed) and Source Score (share of claims correctly cited). source: harvey.ai
25,000+ custom agents execute M&A, due diligence, contract drafting, document review and fund formation inside the platform, including long-horizon agents running multi-step workflows over extended periods. source: harvey.ai
Engineers primarily use Azure OpenAI Service o1-preview, o1-mini and GPT-series models, post-trained and fine-tuned with OpenAI and Microsoft; training its own models is described as something the company is looking at, not something it has done. source: microsoft.com
Harvey reached $300M in annual recurring revenue in May 2026, up from $195M at the end of 2025, with 1,500+ customers in 60+ countries covering half of the Am Law 100 and 50 asset managers. Usage is daily production work rather than trial: 142,000+ lawyers on the platform, 700,000+ tasks executed daily, 50M+ terms extracted weekly, 9.75M active files, monthly queries up 5.5x and weekly active users up fourfold year over year. Median seat count inside an account doubles within twelve months. Harvey-commissioned adoption research reported by Legal IT Insider has 55% of law firms describing it as foundational technology rather than a tool, while 32% of organizations still have fewer than 30% power users. source: sacra.com
A paired axis, not a sixth band: it is never counted in the pattern above and the two are never averaged into one score.
customer: Platform used by 100,000+ lawyers across 1,300+ organisations in 60+ countries, including the majority of the AmLaw 100 and 500+ in-house legal teams. (1,300+ organisations) 2026-03-25source: harvey.ai
funding: Growth round co-led by GIC and Sequoia, taking total raised past $1 billion. ($200M at $11B valuation) 2026-03-25source: harvey.ai
Scouted against the condition: a company that simultaneously owns the customer relationship, the data, the evals, the workflow, and a domain-specific model. Weightless — this profile never affects a signal, and it is not a forecast of returns.
No structured SEC filings. Harvey does not file XBRL with the SEC; see funding rounds under Investors.
Aderant announced an integration between its iTimekeep timekeeping product and Harvey: work performed in Harvey automatically generates a draft time entry in iTimekeep, complete with matter context, duration and an AI-generated narrative, which lawyers review and submit through their firm's standard approval process. Reporting on the announcement notes Harvey and Aderant announced a partnership in December that they described as "market-defining" for bridging software for the business of law and software for the practice of law.
