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Casetext
Legal AI from Thomson Reuters, strongest on US caselaw and legal research.
In short
AI legal research and document analysis from Casetext, now part of Thomson Reuters. Deep integration with Westlaw and a long track record in US legal research distinguish it from newer entrants.
Built for: Compliance/Audit Professionals · Enterprise Operations
| Tier | Model | Key inclusions | Limits |
|---|---|---|---|
| Firm subscription | Per-seat monthly or annual | Research, document analysis, Westlaw integration | Contract-based |
Pricing, version numbers, context-window sizes, and compliance certifications change frequently. Where stated they are accurate as of the as_of date and should be confirmed with the vendor before any procurement or compliance decision. Where they could not be stated confidently they are omitted rather than guessed.
The governance question this raises
Same professional responsibility framing as Harvey: the tool accelerates, the lawyer is still accountable. The Westlaw integration is specifically valuable for the sourcing chain, because a hallucinated citation is immediately checkable rather than requiring a separate lookup. Attorney-client privilege analysis for the data path applies as with any legal AI.
No compliance certifications are listed here. Certification status is vendor-specific and time-specific, so it is stated only where verified rather than assumed. Check the vendor’s trust centre and confirm it covers the specific tier you are buying.
API and SDKs: Via Thomson Reuters platform.
Strengths
Drawbacks
Consider instead: Harvey AI, Microsoft Copilot, ChatGPT
AI legal research and document analysis from Casetext, now part of Thomson Reuters. Deep integration with Westlaw and a long track record in US legal research distinguish it from newer entrants.
Built on GPT-4 with legal-domain fine-tuning and Westlaw retrieval.
Thomson Reuters / Casetext terms govern customer data use. Verify under the current agreement.
The closest comparable tools are Harvey AI, Microsoft Copilot, ChatGPT. Which fits depends on where the work already lives and what the organization's data terms require.
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