Upload your contract
Behind the scenes, our NLP pipeline combines classification, NER, similarity search, and summarization to produce briefs your teams can act on.
Consistent classification and summaries across varied templates and formats.
Supply a contract, our NLP interprets the language, and you receive structured findings and concise briefs.
Phase one: normalize text and layout; segment sections and detect clause boundaries.
Phase two: classify clauses (e.g., termination, indemnity, audit, assignment) and recognize entities (parties, amounts, dates, locations).
Phase three: compute similarities to your playbooks, summarize positions, and highlight risks with rationale and references.
Detect sections and clause boundaries for precise downstream analysis.
Classify clauses and extract entities using NLP models and embeddings.
Summaries, risks, and recommendations mapped to your playbooks.
Encryption, access controls, and optional zero‑retention keep sensitive data protected.
All uploads travel over secure connections and are stored with strong encryption.
Role‑based permissions, audit logs, and SSO/SAML align with enterprise requirements.
Regional data residency and retention policies are configurable.
Consistent outputs across varied templates with clause classification and entity extraction.
Summaries and briefs that translate legal nuance for business stakeholders.
Alignment to playbooks using similarity search and taxonomy mapping.
Portfolio search with embeddings for fast discovery across agreements.
Answers to common questions about clause classification, entity recognition, and summaries.
We combine transformer models for classification and NER with embeddings for similarity search and summarization tuned for legal text.
Yes, text is normalized from scans and multi‑page files before NLP steps.
Findings are aligned to your clause taxonomy and preferred positions for consistent outputs.
We score likelihood and impact with short rationales and references to the contract text.
Natural‑language processing (NLP) reads contracts like a patient first‑pass analyst: it identifies clause types, extracts key details, and produces short briefs you can act on. Under the hood, a contract‑tuned pipeline cleans and segments text, runs clause classification and legal NER (parties, dates, amounts, jurisdictions), compares language to your playbook using similarity search, and then summarizes positions with clear references back to the source lines.
The point isn’t to replace judgment. It’s to remove scavenger hunts and make the obvious… obvious. Over time you’ll see fewer surprise renewals, faster approvals, and far more consistent negotiations.
Start with a practical taxonomy (30–60 clause types for commercial agreements). Distinguish close neighbors—e.g., termination for convenience vs. for cause—so the output actually drives decisions. Good training data pairs examples with counter‑examples and edge cases, so the model learns borders, not just easy wins. Two phrases to naturally include here: nlp contract clause classification and clause taxonomy for contracts.
Contracts encode details in fussy ways—“twelve (12) months,” cross‑references, schedules. A legal NER model should pull parties and roles, notice windows, caps, SLAs, governing law, and more. Rules on top can compute acceptability (e.g., notice ≥ 60 days). Tie each entity to its sentence so reviewers can check with one click. Keywords to weave: legal entity recognition contracts, extraction of parties dates amounts law.
Keep briefs short, neutral, and traceable. One sentence on what the clause does, one on policy fit, one “what to do next,” plus a link to the exact lines. That format becomes the cover note to sales or procurement and cuts back‑and‑forth. Phrase fit: ai contract summarization for lawyers.
Embeddings let you find “meaning‑neighbors,” not just keyword matches. Compare each clause to your preferred language, show a diff, and suggest the closest acceptable variant. This is how playbooks become daily reality rather than PDFs on a drive. Add: similarity search for clause libraries.
Score = likelihood × impact, with a one‑line rationale and a link back to the sentence. Keep the list of automated checks short and useful—renewal window, cap value, carve‑outs, governing law, assignment consent, SLA credits. Measure dismissals and retire noisy checks. Terms: contract risk scoring model legal.
Publish precision and recall per clause and per entity. Add a simple “confirm / edit / dismiss” control so daily use creates tomorrow’s training data. That steady feedback loop is how accuracy improves without giant retrains. Natural terms: nlp quality evaluation precision recall and human in the loop contract analysis.
Standard enterprise controls apply: encryption in transit and at rest, short‑lived processing, role‑based access with audit logs, regional residency, and retention controls (including zero‑retention modes for sensitive matters). Document how PII is minimized or redacted. Keyword fit: contract data privacy retention policies.
Push structured fields and exceptions to CLM (metadata and approvals), ticketing (owners and due dates), and BI/warehouse (dashboards). Even a tiny “exceptions over time” chart helps leaders see progress. Phrases: contract obligations tracking with ai, enterprise contract analytics dashboards.
0–30 days: Pick two doc types (NDA, MSA). Lock taxonomy and import playbook. Label 300–500 examples. Pilot on 100 past contracts; measure precision/recall for five must‑catch checks.
31–60: Tune classification/NER, add brief templates and exception reasons, connect CLM/ticketing, run a weekly 30‑minute labeling session.
61–90: Go live for pilot docs, add dashboards, expand to one more doc type (DPA/SOW), publish accuracy and a feedback channel.
Teams usually report quicker first‑pass reviews on NDAs and standard MSAs, fewer surprise renewals thanks to notice tracking, and cleaner negotiations because carve‑outs/caps are obvious up front. Track before/after on missed renewals, time‑to‑close, and exception rates for 3–5 key clauses. Phrase: playbook alignment contract review.
Do we need data scientists? Not to operate it—legal ops can run the feedback loop; your vendor manages modeling. How much data? Hundreds of well‑labeled examples per clause type get you moving; thousands tighten accuracy. Scans? Supported—measure separately.
