Legal Operations · CLO / General Counsel Priority

AI-Native Contract Intelligence

Legal teams at large enterprises sit on thousands of active contracts with manual review cycles. Obligations are missed, auto-renewals slip, change-of-control risk goes undetected. A contract intelligence layer extracts structured data from every contract, continuously, and surfaces what matters before it costs money.

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70%
Reduction in contract review time
8–12 wk
Deployment timeline
95%+
Obligation extraction accuracy
The Problem

The average Fortune 500 enterprise has 20,000–40,000 active contracts managed across legal, procurement, finance, and business units — often in inconsistent formats, stored in disparate systems, reviewed on manual cycles. The cost is not just attorney hours. It is the exposure: auto-renewal clauses that trigger without notice, change-of-control provisions that surprise acquirers, data processing obligations that violate GDPR without anyone realizing it.

Contract NLI (2021, Stanford NLP) and subsequent multimodal extraction research demonstrate that LLMs fine-tuned on legal clause taxonomies achieve 95%+ accuracy on obligation extraction, outperforming junior associate baselines. A contract intelligence layer ingests all contracts at ingest time, builds a structured obligation registry, and surfaces renewal dates, risk flags, and cross-contract inconsistencies on a rolling basis — not on a quarterly review schedule.

Deployment Specs
Deployment8–12 weeks
Team3–5 engineers + legal SME
StackMultimodal LLM · CLM API · PostgreSQL obligation registry
Target buyerCLO · General Counsel · Head of Legal Operations
Research Basis
Koreeda & Manning, 'ContractNLI: A Dataset for Document-level NLI for Contracts' EMNLP 2021; Hendrycks et al., CUAD: An Expert-Annotated NLP Dataset for Legal Contracts arXiv:2103.06268
ROI Signal
Legal teams report 70% reduction in contract review time on standard clause extraction. Obligation misses and missed auto-renewals drop to near-zero on monitored contract populations. Risk detection latency moves from quarterly review cycles to continuous.

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