Harvey AI
Harvey helps law firms automate complex legal work with AI-powered document drafting and analysis.
Harvey AI provides customized large language models (LLMs) purpose-built for law firms and corporate legal teams to handle complex legal work. The platform handles document drafting, contract analysis, due diligence, and litigation support—tasks requiring deep domain knowledge and multi-step reasoning. Founded by a former O'Melveny & Myers litigator and Google DeepMind researcher, Harvey has embedded legal expertise across its product and go-to-market strategy, with 10% of its team dedicated to change management at customer firms. The company has grown to $190M ARR and achieved an $11B valuation by March 2026.
Problem solved
Attorneys spend excessive time on document drafting, contract review, and due diligence—routine work that requires domain expertise but not creative judgment—consuming 2-10 hours per week per lawyer that could be redirected to higher-value client work.
Target customer
Large law firms, mid-market law firms, and Fortune 500 corporate legal departments with 20+ attorneys requiring advanced document automation and contract intelligence.
Founders
W
Winston Weinberg
CEO & Co-Founder
Former securities and antitrust litigator at O'Melveny & Myers LLP; left Big Law after one year to co-found Harvey.
G
Gabriel Pereyra
President & Co-Founder
Early Brain Resident at Google and research scientist at DeepMind and Meta; developed chain-of-thought prompting techniques leveraged in Harvey's core technology.
Funding history
Seed
$5M
November 2022
Led by OpenAI Startup Fund
· Jeff Dean, Elad Gil, Sarah Guo
Series A
$23M
April 2023
Led by Sequoia Capital
Series B
$80M
December 2023
Led by Elad Gil, Kleiner Perkins
· Sequoia Capital, OpenAI Startup Fund
Series C
$100M
July 2024
Led by Unknown
Series D
$300M
February 2025
Led by Unknown
Series E
$300M
June 2025
Led by Kleiner Perkins, Coatue
· Conviction, Elad Gil, OpenAI, Sequoia
Series F
$160M
December 2025
Led by Andreessen Horowitz
· EQT, WndrCo, Sequoia, Kleiner Perkins, Conviction, Elad Gil
Series G
$200M
March 2026
Led by GIC, Sequoia Capital
Total raised:
$1.22B
Pricing
Subscription SaaS model with per-seat licensing: estimated $1,200–$2,000+ per lawyer per month with 12-month commitments and 20-seat minimums. Custom model development and implementation fees apply. Official pricing not publicly disclosed.
Notable customers
Not disclosed publicly
Tech stack
Bootstrap (UI frameworks)
HTTP/3
Google Analytics (Analytics)
Sentry (Issue trackers)
Google Workspace (Email)
jsDelivr (CDN)
cdnjs (CDN)
Cloudflare (CDN)
Website
Competitors
LexisNexis+ AI
Broader legal research platform with AI features; Harvey is more specialized for document automation and internal law firm operations.
Thomson Reuters AI-Assisted Research
Legacy legal research vendor layering AI on existing research tools; Harvey is purpose-built for transactional work and internal efficiency.
Westlaw AI-Assisted Research
Research-focused AI tool; Harvey focuses on drafting, due diligence, and deal management for transactional practices.
OpenAI ChatGPT (Enterprise)
General-purpose LLM; Harvey provides law-firm-specific fine-tuning, chain-of-thought reasoning, and deep domain knowledge for legal tasks.
Why this matters: Harvey represents one of the most successful recent exits in legal AI, reaching $11B valuation in just 3 years and $190M ARR by 2026. The company's differentiation lies not just in its LLM technology but in its go-to-market strategy: hiring Big Law attorneys across product and sales, embedding customer success lawyers in firms for change management, and solving the specific, high-stakes domain of legal work where accuracy and liability matter intensely. This signals a broader trend where domain expertise + AI + white-glove implementation wins in regulated, high-stakes verticals.
Best for: Large law firms and corporate legal departments performing high-volume transactional work (M&A, real estate, contracts) where attorney time savings directly impact profitability.
Use cases
Contract Due Diligence at Scale
A law firm managing a 200-contract acquisition review manually reviews each document for risk flags and material terms. Harvey identifies discrepancies across hundreds of contracts, surfaces missing clauses, and flags deviations from market standards in minutes instead of days. Associates are freed from repetitive review to focus on negotiation strategy.
Clause Drafting for Deal Closings
Corporate legal teams routinely draft variations of standard clauses (indemnification, reps & warranties, termination conditions) for each transaction. Harvey generates legally-sound, contract-specific clauses based on deal parameters and firm precedent, reducing drafting time from hours to minutes while maintaining quality.
Complex Litigation Document Analysis
A firm managing 50,000 documents in discovery needs to identify which documents are privileged, responsive, and material to key claims. Harvey processes entire document sets, answers questions about litigation scenarios (e.g., 'Which emails discuss intent to commit antitrust violations?'), and surfaces the most critical evidence for attorney review.
Internal Legal Team Efficiency
A 100-lawyer firm internally saves 20-40 attorney hours per week using Harvey for routine work (NDAs, employment agreements, vendor contracts). This capacity is reallocated to client-facing work without adding headcount, improving profitability and lawyer utilization.
Alternatives
Casetext CoCounsel
AI legal assistant focused on litigation research and brief writing; Harvey emphasizes transactional work and internal operations automation.
LawGeex
AI-powered contract review platform with narrower use case scope (contract analysis); Harvey is broader, covering drafting, due diligence, and litigation support.
Kira Systems
Machine learning platform for due diligence and contract intelligence; Harvey offers broader LLM-based capabilities for drafting and reasoning beyond document classification.
LexCheck
AI contract review tool; Harvey is enterprise-grade with customized models and deeper integration into law firm workflows.
FAQ
What does Harvey AI do? +
Harvey provides customized large language models designed for law firms and corporate legal teams. The platform automates complex legal tasks including document drafting (clauses, contracts), contract analysis and due diligence, litigation support (document review, scenario analysis), and deal management. It combines AI reasoning with legal domain knowledge, allowing attorneys to focus on high-value work instead of routine drafting and review.
How much does Harvey cost? +
Harvey's pricing is not publicly disclosed. Based on market intelligence, the platform costs approximately $1,200–$2,000+ per lawyer per month on 12-month commitments with 20-seat minimums. Custom pricing applies for model development, implementation, and customer success services (which consume ~10% of Harvey's team).
What time savings does Harvey deliver? +
Harvey's internal legal team reports saving 20-40 attorney hours per week. Customer feedback consistently reports 2-10 hours of savings per attorney per week, depending on practice area and usage. Savings are most pronounced for high-volume transactional work (M&A, real estate, vendor contracts).
Who uses Harvey? +
Target customers are large law firms (100+ attorneys), mid-market practices with substantial transactional work, and Fortune 500 corporate legal departments. Specific customer names are not disclosed publicly, but the company has hired sales talent from Big Law firms and embedded former attorneys in product and customer success roles.
How does Harvey compare to ChatGPT or general LLMs? +
General LLMs like ChatGPT lack legal domain specialization and produce inconsistent results on complex legal reasoning tasks. Harvey is fine-tuned on legal corpora and trained using chain-of-thought prompting for multi-step legal reasoning (e.g., analyzing 100+ contracts for discrepancies). The platform also includes law-firm-specific integrations, change management support, and accountability for output quality—critical for risk-averse legal buyers.
What practice areas does Harvey serve? +
Harvey is strongest in transactional work: M&A, real estate, corporate governance, and vendor management. It also supports litigation (document review, scenario analysis) and general counsel tasks. The platform is less suited to courtroom-facing work (oral arguments, trial strategy).
How does Harvey ensure legal accuracy? +
Harvey's models are trained with chain-of-thought prompting and fine-tuned on legal reasoning tasks. The company validates output against real attorney feedback (e.g., 86 of 100 landlord-tenant answers were approved by two of three attorneys with zero edits in early testing). Harvey also embeds lawyers in customer success roles to monitor adoption and quality in real-world use.
Tags
legal AI
LLM
contract analysis
document automation
due diligence
law firm software
legal tech