Harvey
Harvey supports legal research, drafting, contract analysis, due diligence, and review.
Overview
- Transparency
- 8 / 10
- Trust
- 98 / 100
Trains on your data
- Free
- No
- Paid
- No
- Opt-out
- Not captured
Incidents
00
Assessed SEP 21, 2026 · Checked by hand SEP 21, 2026
SOC 2, ISO 27001 and GDPR are confirmed with no default model training and provider ZDR; the alleged 100,000-file incident is misattributed from Filevine.
Trust breakdown
98 / 100
Overall trust score
Data and privacy
Trains on your data
No training on user data
Storage regions
Sub-processors
Data retention
Harvey requires Zero Data Retention (ZDR) by model providers.
Deletion: Within 30 days of either (i) termination of the Service; or (ii) upon Your reasonable request; Harvey shall, and shall direct each Subprocessor to, return to You or delete the Customer Personal Data, unless Harvey is required by law to retain such Customer Personal Data.
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Harvey utilizes a multi-model architecture, integrating leading foundation models from Anthropic, Google DeepMind, and OpenAI. They also develop their own proprietary models through post-training and custom training efforts, often in partnership with other companies like OpenAI, Fireworks, Applied Compute, and Engram. These proprietary models are built on top of existing base models (e.g., Kimi K3, GLM 5.2, Qwen).
Integrations and access
Integrations
Available on
Pricing
Enterprise
Custom
Pricing is not publicly available and is provided through a sales-led enterprise model. Reported estimates suggest per-seat pricing varies significantly based on firm size, with smaller firms potentially paying $1,000-$2,000 per user per month, and larger firms (Am Law 100) paying $100-$200 per user per month with volume discounts. There are reported seat minimums (e.g., 20-50 seats) and annual contract commitments. Total Cost of Ownership (TCO) can include implementation, training, and optional fine-tuning fees.
Prices as listed on SEP 21, 2026; check the vendor's page.
Moat and openness
Moat
- Proprietary Model
- Proprietary Data
- Network Effects
- Switching Costmoderate
- Unique UX
- Distributionstrong
Harvey demonstrates a moderate moat, primarily driven by its specialized multi-model architecture, custom legal-specific training, and strong enterprise distribution with notable customers. While its underlying models are not entirely proprietary, the custom post-training and deep integrations create some switching costs. However, the lack of proprietary data and network effects limits a stronger defensibility.
Openness
- Open model weights
- Published research
- Open source contributions
- Transparency reports
- Public safety evals
Harvey demonstrates moderate openness. While they do not open-source their model weights, they actively publish research findings, contribute to open-source projects like their Legal Agent Benchmark (LAB), and conduct public safety evaluations through certifications and audits. Their Trust Center provides transparent access to certifications and security practices.
Timeline
- 2025-12Shared Spaces launched, enabling secure real-time collaboration between firms and clients.
- 2025-11New and improved reasoning capabilities introduced, enabling iterative, agentic searches across large document sets and deep analysis over multiple sources.
- 2025-09Harvey mobile app announced, bringing AI capabilities to phones and tablets with mobile-specific features like Voice to Prompt and document scanning.
- 2025-08Microsoft 365 Integrations launched, including a new Harvey for Outlook Add-In and enhanced functionality for the Harvey for Word Add-in.
- 2025-06Workflow Builder launched, allowing legal teams to create custom AI-powered workflows.
- 2025-05Later Stage VC (Series E) funding round completed.
- 2025-02Later Stage VC (Series D) funding round completed.
- 2024-07Later Stage VC (Series C) funding round completed.
Show all
- 2023PwC builds on Harvey, and OpenAI joins the alliance.
- 2022First-year associate and roommate test GPT-3 on landlord-tenant law, leading to the inception of Harvey.
Company
Harvey
- Founded
- 2022
- HQ
- San Francisco, California, US
- Site
- harvey.ai
Popularity
427.7K
monthly visits
Notable customers
Harvey is a leading AI copilot for large law firms and corporate legal departments, offering specialized capabilities for document analysis, legal research, and multi-language translation.
Value and ROI
What it should move: Lawyers spend less time researching matters, preparing first drafts, and reviewing documents.
Best fit
It fits legal teams with matter controls and lawyers who review generated work.
The catch
Harvey receives privileged and confidential material. Teams need matter separation, source checks, jurisdiction checks, and lawyer review.
- A&O Shearman: Uses Harvey for contract analysis, due diligence and legal document review.[1]
- The Adecco Group: Uses Harvey for legal research, contract drafting and regulatory compliance workflows.[2]
In our stacks for