Are your attorneys spending review time confirming standard clauses are present rather than analysing the clauses that create real risk?
Is contract review time the bottleneck that slows your commercial deal cycle, and is that bottleneck growing as contract volume increases?
When a counterparty slips a non-standard limitation of liability clause into a routine agreement, does your review process reliably catch it?
AI Contract Review for Legal Teams
Contract review AI that reads every clause, flags deviations from your playbook, identifies missing provisions, and surfaces the positions that need attorney judgment -- so reviewers focus on the exceptions, not on confirming that boilerplate is boilerplate.
For legal teams reviewing high volumes of commercial contracts where the same standard clauses appear in 80% of documents and the same 5 risk positions appear in the ones that need work.
Clause extraction and classification across all standard contract sections without manual identification
Deviation detection against your playbook -- flagging where counterparty language differs from your standard positions
Missing provision alerts for required clauses not present in the counterparty draft
Risk scoring by section with attorney attention directed to the highest-risk deviations
RaftLabs builds AI contract review systems for legal teams including clause extraction and classification, deviation detection against your playbook positions, missing provision identification, risk scoring by contract section, redline generation for non-standard positions, and integration with your contract management system or CLM platform. AI contract review reduces time-per-contract review by 40-70% for routine commercial agreements by surfacing the clauses that need attorney attention and annotating those that are standard. Most contract review AI projects deliver in 8-14 weeks at a fixed cost.
40-70%Review time reduction on routine contracts
·100+Products shipped
·24+Industries served
·FixedCost delivery
The contract review problem is a volume and attention problem
A 30-page NDA still requires a full read to confirm the standard clauses are present and the non-standard ones are not. A legal team reviewing 300 commercial contracts per year is spending significant attorney time on work that does not require attorney judgment -- confirming the confidentiality clause is present, the liability cap is standard, the governing law is correct -- before they can get to the clauses that actually need their attention.
AI contract review inverts this. The AI reads every clause, classifies it, compares it against your playbook, and flags the deviations and missing provisions. The attorney reviews the flagged items, not the whole document. The routine confirmation work disappears. The nuanced judgment work remains.
What we build
Clause extraction and classification
AI that reads a contract and identifies every substantive clause, classifying it by type: limitation of liability, indemnification, confidentiality, IP ownership, termination, governing law, dispute resolution, warranty, data protection, and the other standard sections your playbook covers. Clause text extracted and presented with its classification so reviewers can navigate directly to any section type without scrolling the full document. Works on uploaded PDFs, Word documents, and text files. Extraction quality evaluated and validated against your contract library before production deployment.
Playbook deviation detection
Comparison of extracted clauses against your defined playbook positions. Your playbook documents your standard positions: acceptable language for each clause type, positions you will not accept, and positions that require escalation. The AI compares counterparty language against these positions and flags deviations with a severity classification: standard (no action required), deviation (attorney review needed), or high risk (escalation required). The deviation report replaces the line-by-line manual comparison that currently occupies the first two-thirds of the review.
Missing provision detection
Detection of required provisions absent from the counterparty draft. Your standard agreement checklist defines the provisions that must be present: data processing clauses for contracts involving personal data, specific warranty disclaimers, required IP assignment language, defined governing law. When the AI does not find a required provision in the counterparty document, it flags the absence with the missing provision identified. Prevents the scenario where a required clause is silently absent from the contract because the reviewer read it and noticed only the clauses that were there.
Risk scoring and prioritisation
A risk score per contract section and an overall contract risk rating that directs attorney attention to where it is most needed. Risk scoring based on: deviation severity from playbook, clause category risk weight (limitation of liability typically higher risk than governing law), number of non-standard positions, and missing required provisions. High-risk contracts surface at the top of the review queue. Within a contract, high-risk sections surface first. For legal teams reviewing many contracts simultaneously, risk prioritisation ensures the riskiest positions receive attention first rather than being sequenced by the order documents arrive.
Redline generation
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 language, the system generates a redline replacing the counterparty language with your preferred position. The attorney reviews and approves the redline before it goes into the negotiation document -- the AI generates the first draft of the redline, not the final version. Reduces the time from clause identification to counterparty response for the most common deviation types.
CLM and workflow integration
Integration with your contract management system or CLM platform. Contracts submitted for review through your CLM trigger the AI review automatically. AI review results are attached to the contract record. Reviewer notifications when AI review is complete with a link to the flagged items. Accepted and rejected AI suggestions tracked for model improvement. For organisations without a CLM, we build a lightweight review workflow interface that tracks contracts through the review process. The AI review system is not a standalone tool -- it is integrated into how your team currently receives, processes, and stores contracts.
Frequently asked questions
AI contract review delivers the highest time reduction for contracts with consistent structure and high volume: NDAs, standard service agreements, vendor contracts, SaaS subscriptions, and commercial contracts that follow your organisation's standard templates. These contracts have predictable clause structures, known deviation patterns, and playbook positions that have been defined over many reviews. For highly negotiated, bespoke agreements (M&A documents, complex financing arrangements, novel commercial structures), AI review provides useful extraction and flagging but requires more significant attorney engagement because the structure is less predictable and the deviations are more complex. We assess your contract mix during scoping and design the system for the contract types where it delivers the highest value.
Playbook encoding is a structured exercise at the start of the project. Your legal team defines: the clause types you care about for each contract category, the acceptable language ranges for each clause type, the positions you will not accept, and the risk weight of each deviation. This is formalised into a playbook document that becomes the training reference for the deviation detection model. We review the playbook with your team, resolve ambiguities, and validate that the encoded positions match how your team actually reviews contracts. The playbook is versioned and updated when your positions change. The AI learns your playbook, not a generic legal standard.
Extraction accuracy for well-defined, consistently structured clause types in standard commercial contracts is typically 92-97%. Deviation detection accuracy depends on playbook clarity and counterparty language variability. For your most common contract types with well-defined playbook positions, deviation detection precision (flagged deviations that are genuine deviations) runs 85-95% and recall (genuine deviations that are flagged) runs 90-98%. These numbers are measured and reported to your team during the evaluation phase before production deployment. For clause types or contract formats where accuracy is below threshold, we iterate on the model before deployment rather than releasing a system that creates more review work than it saves.
The system includes confidence scoring on its review output. For clauses where the extraction or classification confidence is low, the system flags the clause for manual review rather than presenting a potentially incorrect classification as certain. For contract types that fall outside the trained scope -- a format the system has not seen before, a language that was not included in training -- the system surfaces this as a low-confidence review requiring full manual attention. The goal is that attorneys can trust the high-confidence output and know which items still require full attention. A system that presents uncertain classifications as certain creates a false sense of completeness, which is worse than no AI review at all.
Tell us your contract volume, the contract types you review most frequently, and how much attorney time the review cycle currently takes. We will scope the system and give you a fixed cost.
