AI Contract Review for Legal Teams

LLM-based clause extraction using GPT-4o or Claude 3.5 Sonnet with structured JSON output and JSON Schema validation -- each extracted clause is returned as a typed JSON object with fields for clause type, extracted text, page reference, and a confidence score. JSON Schema validation at the API response layer ensures the structured output is machine-readable and consistent regardless of how the underlying LLM formats its response, preventing hallucinated field names or missing required properties from reaching the review interface. Named Entity Recognition (NER) using spaCy or AWS Comprehend Legal identifies contract parties, effective dates, monetary values, payment terms, and notice periods within the extracted text -- so party names, contract values, and key dates are surfaced as structured data fields, not just free text for the reviewer to scan manually. Clause types covered include limitation of liability, indemnification scope and carve-outs, confidentiality obligations and exceptions, IP ownership and assignment breadth, termination triggers and notice requirements, governing law and jurisdiction, dispute resolution mechanism, warranty scope and disclaimers, data protection and GDPR DPA obligations, auto-renewal provisions, and any custom clause types defined in your playbook. Clause text is extracted and presented with its classification so reviewers navigate directly to any section type without scrolling the full document. Works on uploaded PDFs, Word documents, and text files. Extraction quality is evaluated against your contract library during the validation phase before production deployment.

Comparison of extracted clauses against your defined playbook positions. Your playbook documents your standard positions per clause type: acceptable language ranges, positions you will accept with modification, positions you will not accept, and positions that require escalation to senior counsel or the business. Risk flagging is configurable per clause type: non-standard indemnification scope (indemnifying the counterparty for gross negligence or wilful misconduct), uncapped liability exposure (limitation of liability clause that does not cap the counterparty's aggregate liability), automatic renewal without adequate notice (auto-renewal triggered with less than the minimum notice period your playbook requires), and IP assignment breadth (assignment language that transfers more IP than the relationship requires, or that includes background IP not created under the contract). The AI compares counterparty language against these configured positions and flags deviations with a three-tier severity classification: standard (no action required), deviation (attorney review needed), or high risk (escalation required). Each flagged deviation includes the extracted counterparty language, the playbook position it deviates from, and the specific risk the deviation creates -- so the reviewing attorney sees not just that there is a deviation but why it matters. The deviation report replaces the line-by-line manual comparison that currently occupies the first two-thirds of a review and requires the reviewer to hold the entire playbook in memory while reading.

Detection of required provisions absent from the counterparty draft. Your standard agreement checklist defines the provisions that must be present for each contract category: GDPR Data Processing Agreement (DPA) clauses for contracts involving personal data processing (required under GDPR Article 28 when appointing a data processor), specific warranty disclaimers for software and technology contracts, required IP assignment language for development or services agreements, defined governing law and jurisdiction, mandatory dispute resolution mechanism, and any contract-category-specific provisions your legal team has identified as non-negotiable. GDPR DPA review specialisation handles the detailed requirements of Article 28 DPA clauses -- the system checks for the required sub-processor notification obligations, data subject rights support obligations, security obligations, audit rights, and deletion or return obligations that a compliant DPA must include. When the AI does not find a required provision in the counterparty document, it flags the absence with the specific missing provision identified and the reason it is required. Clause comparison against your precedent library identifies whether a present but non-standard clause has been used successfully in a previous contract, surfacing historical context that may inform the attorney's decision on whether to accept or push back. This prevents the scenario where a required clause is silently absent because the reviewer noticed only what was there.

A confidence-scored risk assessment per contract section and an overall contract risk rating that directs attorney attention to where it is most needed. Risk scoring factors: deviation severity from playbook position (high-risk deviations carry more weight than standard deviations), clause category risk weight (uncapped liability and broad indemnification carry higher weights than governing law or notice period deviations), number of non-standard positions across the contract, presence or absence of required provisions, and counterparty-specific risk history if the organisation has reviewed contracts from the same counterparty previously. Each extracted and classified clause carries a confidence score -- high-confidence extractions are presented for attorney review of the flagged deviation; low-confidence extractions are flagged as requiring full manual attention rather than being presented as certain classifications. The attorney review queue for low-confidence extractions is separated from the deviation review queue so attorneys know which items are AI-confident and which require their independent judgment without AI assistance. High-risk contracts surface at the top of the review queue. Within a contract, high-risk sections surface first. Clause comparison against your precedent library adds a historical dimension to the risk score -- a clause that has been accepted in five previous contracts with this counterparty is scored differently from one that has never appeared before. For legal teams reviewing many contracts simultaneously, risk prioritisation ensures the riskiest positions receive attorney attention first.

Automated redline generation for non-standard clauses where your playbook defines an alternative position. When a counterparty clause deviates from your standard position and your playbook specifies the preferred fallback language, the system generates a redline comparing the counterparty language against your preferred position in tracked-changes format. The redline is generated using GPT-4o or Claude 3.5 Sonnet with your playbook's fallback language as the reference input, producing a Word-compatible tracked-changes document that attorneys can open directly in their document management system. For organisations using iManage Work or NetDocuments as their document management platform, redlined documents are saved back to the matter workspace automatically. For organisations using SharePoint or OneDrive as their document store, the integration routes through SharePoint REST APIs to save the redline alongside the original counterparty draft. The attorney reviews and approves each redline before it goes into the negotiation response -- the AI generates the first draft of the redline, not the final version, and attorney oversight is required before any redline leaves the organisation. Accepted and rejected redline suggestions are tracked and fed back to refine the playbook encoding over time, so the system learns which redlines your attorneys accept and which they modify. Reduces the time from clause identification to counterparty response for the most common deviation types from hours to minutes.

Integration with your contract management system or CLM platform so AI review is a seamless step in the existing contract lifecycle, not a separate tool that requires manual export and re-import. For iManage Work users, contracts received into a matter workspace trigger the AI review via iManage REST API webhook, and results are saved as annotations and a summary document within the same matter. For NetDocuments users, the integration uses NetDocuments REST APIs to retrieve documents from a configured cabinet, trigger review, and post results. For SharePoint-based document management, the integration uses Microsoft Graph API to monitor a designated document library and process new contracts on arrival. Reviewer notifications are sent via the CLM's native notification system or via email when AI review is complete, with a direct link to the flagged items summary. For Bluebook or OSCOLA citation checking in contracts that include legal references, the citation validation module checks extracted citations against the relevant citation format standard and flags non-conforming references. For organisations without a CLM, we build a lightweight review workflow interface that manages contract intake, tracks status through the review stages, and stores the AI review results alongside the contract document. Accepted and rejected AI suggestions are tracked per clause type and fed back to the playbook refinement process so deviation detection accuracy improves over time. The AI review system integrates into how your team currently receives, processes, and stores contracts rather than requiring a workflow change.