Extract obligations, flag deviations, and compare contracts against your playbook.
A corporate legal team managing 500+ active contracts faces a review bottleneck that scales linearly with headcount. Every new vendor agreement requires a junior associate to read 30-80 pages, extract key terms (payment, termination, liability caps, IP assignment, indemnification), compare against the firm's standard playbook, and flag deviations. This takes 2-4 hours per contract.
During M&A due diligence the problem compounds: teams must review hundreds of contracts in weeks. Kira Systems (now Litera) demonstrated ML-based extraction at scale — used by 70 of the top 100 global law firms. Luminance built anomaly detection for cross-border reviews. Harvey AI showed LLM-based Q&A over document sets surfaces answers keyword search misses.
The gap is making these capabilities accessible to mid-market firms without enterprise platform licenses or six-month implementations.
This agent processes contracts in PDF, Word, or scanned format and extracts a structured data model: parties, effective dates, payment terms, termination provisions, liability caps, indemnification clauses, IP assignment, non-compete/non-solicit provisions, governing law, and dispute resolution mechanisms using fine-tuned transformer models trained on legal corpora.
Beyond extraction, it compares each contract against your clause library and standard playbook. Deviations are flagged with severity scores: a missing limitation of liability clause gets a critical flag; a non-standard notice period gets informational. For M&A due diligence, it processes entire data rooms and generates a risk matrix across the full contract set.
Reports map to how legal teams actually review — clause-by-clause comparison with your standards, deviation flags, and suggested redline language from your approved clause library.
Built on Anthropic Claude for clause-level reasoning, with spaCy handling named entity extraction for parties, dates, and obligations. A FastAPI service manages document ingestion from iManage or NetDocuments, routes contracts through extraction pipelines, and stores structured clause data in PostgreSQL with Elasticsearch for cross-portfolio search. We configure the extraction schema against your playbook in week one and run parallel validation on your existing contracts before go-live — full setup in 2-3 weeks.
Processes PDF, DOCX, and scanned contracts using layout-aware parsing that preserves clause hierarchy, defined terms, and nested provisions. Flat-text extraction loses table structures and cross-references — this doesn't.
Each extracted clause is compared against your standard positions and fallback language. Deviations are flagged with severity levels (informational, negotiation risk, deal blocker) and paired with approved alternative language pulled from your clause library.
Batch-processes hundreds of contracts in a data room, then generates cross-contract risk matrices showing liability caps, indemnification exposure, and change-of-control provisions across the full portfolio. Cuts M&A first-pass review from weeks to days.
Identifies contractual obligations tied to specific deadlines — renewal windows, notice periods, payment milestones, regulatory reporting dates — and pushes them into your calendar or matter management system. Proactive, not reactive.
Anonymized work with hard metrics — NDA-bound, no client names.
82%
Time Reduction
93%
Extraction Accuracy
2.8x
Review Throughput
“We were spending six hours per contract on review work that should have been automated years ago. The extraction accuracy is high enough that our lawyers now start from the AI output and spend their time on the obligations that actually require judgement.”
— Managing Associate, Corporate Practice Group
We custom-build each agent to fit your data, your rules, and your existing systems.
Free 30-min scoping call
Fordel Studios