Upload your contract
We combine supervised classifiers, named‑entity recognizers, embeddings, and summarizers to deliver consistent, verifiable outputs across agreements.
From varied templates to standardized briefs and risk insights.
Provide a contract, our ML pipeline interprets the language, and you receive structured findings and concise briefs.
Stage 1: preprocess and segment the document into sections and clause candidates.
Stage 2: apply clause classifiers and entity detectors trained on legal corpora.
Stage 3: summarize and compare against playbooks to surface exceptions and risk with rationale.
Segment sections and detect clause boundaries; normalize text for modeling.
Run clause and entity models to interpret obligations, parties, dates, amounts, and more.
Produce summaries, risk notes, and playbook alignment your teams can use.
Encryption, access controls, and optional zero‑retention keep sensitive data protected.
All uploads use secure connections and are stored with strong encryption.
Role‑based access, audit logs, and SSO/SAML support enterprise needs.
Regional residency and retention options are configurable.
Consistent clause classification, entity extraction, and summaries across templates.
Briefs that translate legal nuance for business decisions.
Similarity search maps findings to your playbooks and preferred positions.
Portfolio‑level search and analytics powered by embeddings.
Answers to common questions about classifiers, entities, and summaries for contracts.
Yes. We can adapt taxonomies and fine‑tune models to your clause library and policies.
Accuracy varies by domain; we report precision/recall and provide human‑verifiable rationales.
Yes, text is normalized from scans and long PDFs before ML steps.
We score likelihood and impact with concise rationales and references.
Jan 14, 2026
The limitation of liability clause is the line between a manageable hiccup and a serious hit to the business. Caps, carve-outs, and excluded damages (like indirect, consequential, or lost profits) dec...
Read more →Jan 13, 2026
Non-compete agreements are getting a lot of heat right now. Between the FTC’s 2024 rule and a maze of state laws, it’s hard to know what’s safe and what’s going to cause headaches later. So, can AI au...
Read more →Jan 12, 2026
If your renewals, notice windows, and deliverables are scattered across spreadsheets and inboxes, you’re probably bleeding money and inviting last‑minute chaos.The upside: modern AI can read your cont...
Read more →Machine learning (ML) turns piles of contracts into structured data and helpful notes. It excels at repeatable work: clause classification, legal entity recognition (parties, dates, amounts), and short, traceable briefs. A practical pipeline cleans text, segments sections, classifies clauses, extracts entities, runs similarity search against your playbook, then summarizes positions and flags exceptions with reasons.
Teams use this to cut first‑pass review time, reduce surprise renewals, and standardize negotiations. It doesn’t replace judgment—ML simply handles the hunt-and-verify steps so humans focus on decisions.
Three building blocks carry most of the load. Clause classification labels text as termination, limitation of liability, governing law, and so on—granularity enables better routing and checks. Legal NER extracts parties, affiliates, dates, notice windows, caps, SLAs, and jurisdictions (including quirky formatting like “twelve (12) months”). Finally, a brief summarizes the position and links back to the exact lines. Similarity search over your clause library spots wording that “means the same” or drifts from policy.
Start with a realistic taxonomy—30–60 clause types for MSAs/SOWs; fewer for NDAs/DPAs. Write labeling rules with examples and tie‑breakers and measure inter‑rater agreement. Include negative and edge cases so models learn the boundaries, not just the obvious center.
Mix your own contracts with curated public samples. Clean OCR, strip artifacts, and split data by document (not sentences) for honest generalization. Keep a weekly QA loop: spot‑check samples, label model mistakes, and fold them back in. Track precision/recall by clause and entity so regressions are obvious.
Publish precision and recall tables—especially for “spicy” clauses like liability. Add simple “confirm / edit / dismiss” controls to turn daily reviews into new training data. Human‑in‑the‑loop keeps accuracy improving without giant retrains.
Sort work by likelihood × impact with one‑sentence rationales and links to the source lines. Prioritize 5–7 checks people actually act on (renewal window, cap value, carve‑outs, governing law, assignment consent, SLA credits, data processing limits). Measure what’s caught vs. dismissed and retire noisy checks.
Encode policy as rules and run them on every contract. Pair with embeddings‑based similarity to your clause library so reviewers see “what this is closest to,” the diff, and a recommendation. This is where consistency compounds across the portfolio.
Keep briefs short, neutral, and traceable. One line on what the clause does, one line on policy fit, one suggested next step, and a link to the lines used. These become the cover notes to sales/procurement and cut back-and-forth.
Push structured outputs to the tools that move work: update CLM metadata, create tickets for owners/dates, and store fields/exceptions in your warehouse for dashboards. A tiny “exceptions over time” chart makes value obvious to leaders.
Use encryption in transit and at rest, short‑lived processing, role‑based access with audit logs, regional data residency, and retention controls (including zero‑retention). Document PII handling/redaction paths for compliance reviews.
0–30 days: Pick two doc types (NDA, MSA). Lock taxonomy, import playbook, label 300–500 examples, pilot 100 past contracts; measure precision/recall for five checks. 31–60: Tune NER/classification, add summaries and exception reasons, connect CLM/ticketing, run weekly 30‑minute labeling. 61–90: Go live, add dashboards, expand to one more doc type (DPA/SOW), publish accuracy and a feedback channel.
