A source-grounded workspace for legal teams: extract clauses from hundreds of agreements, flag deviations from your gold-standard templates, run due diligence at speed without losing defensibility.
No credit card. Confidentiality assured by architecture.
M&A data rooms. Discovery doc sets. Contract review for a portfolio of 200 vendor agreements. The judgment that makes you valuable is buried under hours of reading you don't want to do, and your client doesn't want to pay for.
The job isn't reading faster. It's not reading at all when reading isn't the work.
Drop 200 agreements into a dataset. Run the Data Extractor with your standard schema: parties, effective dates, termination, liability caps, change-of-control. Get a spreadsheet you'd hand an associate. Now spend the morning on the deals that warrant judgment, not on the bookkeeping.
How legal teams use it
Turn a folder of contracts into a structured table: parties, dates, clauses, caps. Run across hundreds of documents in one batch. Output is a spreadsheet, not a chat transcript.
Audit third-party agreements against your firm's gold-standard templates. docAnalyzer flags material deviations, missing protective clauses, and unfavorable terms, so you negotiate from knowledge.
Describe what you need to surface ("Agreements with Change of Control provisions", "Contracts requiring outside-counsel review") and pull the matching set in seconds. Triage before you read.
Client confidentiality is non-negotiable.
Per-tenant isolation, no training on customer content, session-scoped artifacts. Your data is yours: visible only to your authenticated users, never pooled across customers.
If the AI is wrong on a clause, the answer is worthless.
Every cited claim is clickable. One click opens the agreement at the exact section. Verification is faster than guesswork. Turn the context-adherence dial high to keep the model strictly inside your repository.
Our agreements span multiple formats and 500-page exhibits.
PDFs, Word, scanned docs (OCR'd automatically), Excel exhibits: all indexed once at upload, so search time grows much slower than your dataset and the same workflows work across formats.
Upload a folder from one active matter. Run the Data Extractor with your standard schema. See if what comes back is faster than how you do it today.