Harvey
Legal AI software with agents, a document Vault and memory, sold to law firms and in-house teams.
How the 3.6 was reached
- Quality of output6/10
42% of AmLaw 100 firms use it and it scored 38/50 on an independent benchmark, but citation hallucinations are reported. haqq.ai
- Ease of use3/10
Users report workflow mismatch, limited customization and vault caps causing adoption friction; no docs or self-serve trial found. ragbase.ai
- Pricing value2/10
No public pricing; estimated $1,200-$2,000+ per seat monthly and about $175,000 median annual contract. vaquill.ai
- API and integration quality2/10
No public API, docs or integrations found on the pages checked or by search. harvey.ai
- Solves the problem it claims to solve5/10
Agents and Vault are described by the vendor, but independent reviews document fabricated case law and low seat utilization. ragbase.ai
3.6/10 is the mean of the 5 criteria that apply. Scored on the five-criteria rubric v1, 29 September 2026. How scoring works
Pricing
Key Features
- Agents that carry out legal work end to end, per the vendor
- Vault for storing and bulk-analyzing legal documents
- Memory that carries user preferences across the product
- Contract intelligence tools
- Serves law firms, in-house teams and litigation practices
Pros & Cons
Pros
- 42% of AmLaw 100 firms use it
- Scored 38/50 on an independent benchmark
- Broad coverage of firm, in-house and litigation work
- Well funded, with a reported $550M raise at a $15.5B valuation
Cons
- No public pricing; estimates run $1,200-$2,000+ per seat monthly
- Reviewers report fabricated case citations
- Users cite limited customization and vault caps
- No public API, docs or self-serve trial found
Fits large firms with budget and a tolerance for a sales-led rollout. The low score reflects opaque pricing, missing docs and reported hallucinations, so smaller teams should look elsewhere.
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