AI contract redlining software proposes tracked changes to a contract based on your playbook: preferred clauses, fallback positions and red lines. To choose one, test it on your own contracts and playbook, and check that every edit is explainable, reviewable and logged.
The strongest tools fit how lawyers already work, usually Microsoft Word with tracked changes and a document management system. The weakest produce plausible edits with no link to a policy.
This checklist covers capability, integration, security, ethics and a scored pilot. It is general information, not legal advice.
What AI Redlining Should Do
Redlining goes further than clause extraction. The software must decide what to change, propose wording and explain why, in a format the reviewing lawyer can accept or reject.
• Detect: find each clause covered by the playbook
• Compare: check it against preferred and fallback positions
• Propose: insert tracked changes or comments with replacement text
• Explain: cite the playbook rule behind each edit
• Route: escalate red-line issues to the right approver
Our contract playbook template explains how to write rules a tool can apply.
Buyer Checklist: Capability
• Applies your playbook, not only a vendor default
• Supports preferred, fallback and walk-away positions per clause
• Produces native Word tracked changes and comments
• Links each edit to the rule that triggered it
• Handles your paper and third-party paper
• Flags missing clauses, not only bad ones
• Lets you update the playbook without vendor engineering
• Supports the contract types and governing laws you handle
Buyer Checklist: Integration
• Works inside Microsoft Word or your editor of choice
• Connects to your DMS, such as iManage or NetDocuments, if you use one
• Writes back versions without breaking DMS version history
• Supports single sign-on and your user groups
• Exports review data for reporting
Buyer Checklist: Security and Confidentiality
ABA Formal Opinion 512 says lawyers must evaluate the risk that client information entered into a generative AI tool could be disclosed or accessed by others, and in many cases get informed consent before using self-learning tools with client data.
• Confirms in the contract whether your data trains any model
• States data retention and deletion terms
• Documents where data is processed and stored
• Provides access logs you can review
• Supports matter-level or client-level access restrictions
• Ask for current security attestations and read the scope yourself
Buyer Checklist: Reviewer Control and Audit
• Every AI edit is visible as a suggestion until a person accepts it
• Records who accepted, rejected or changed each edit
• Keeps a history of playbook versions used for each review
• Allows a lawyer to override and annotate the reason
• Produces an audit record that can be exported
Professional Duties to Check
• Rule 1.1, competence: lawyers should understand the tool's capabilities and limits
• Rule 1.6, confidentiality: assess data handling before entering client information
• Rules 5.1 and 5.3, supervision: set policies for lawyers and nonlawyers using the tool, and supervise vendors
• Rule 1.5, fees: charge reasonably when the tool reduces time
• State bars: several have issued their own AI guidance, so check your jurisdiction
Worked Example: A Pilot Scorecard
Illustrative example: a hypothetical scoring design, not results for any vendor. A legal team pilots two tools on the same 25 NDAs and 10 vendor agreements already reviewed by its lawyers.
| Criterion | Weight | How to score |
|---|---|---|
| Playbook accuracy | 30% | Share of AI edits a lawyer would keep unchanged |
| Missed issues | 25% | Playbook issues the lawyer found that the tool did not |
| Explanation quality | 15% | Edits with a correct rule citation |
| Workflow fit | 15% | Reviewer rating of Word and DMS experience |
| Confidentiality terms | 15% | Pass or fail against the security checklist |
Scoring: each criterion is scored 1 to 5 and multiplied by its weight. A tool that fails the confidentiality check is excluded whatever its total score.
Use contracts already reviewed by your lawyers so you have a baseline. Record time saved only if it is measured the same way for both tools and for the baseline.
Common Mistakes to Avoid
• Piloting without a playbook: you end up judging the vendor's standards, not yours
• Scoring on demo contracts: vendor samples rarely look like your third-party paper
• Counting edits instead of correct edits: more redlines are not better redlines
• Skipping the confidentiality review: a strong tool with weak data terms may be unusable for client matters
• No owner for the playbook: rules go stale and the tool keeps applying old positions
Questions to Ask Any Redlining Vendor
• Can every edit be traced to a playbook rule we wrote?
• Does any of our data train a model, now or in future versions?
• How does the tool work with our DMS version history?
• What audit record is kept of reviewer accept and reject decisions?
• How quickly can we update the playbook when our positions change?
Rollout Sequence
Start with one contract type, a written playbook and a small group of reviewers. Expand to other contract types only after a separate pilot on each, with the same scorecard and the same confidentiality checks.
How We Evaluated This Guidance
We reviewed current vendor and standards documentation listed under Sources, read on October 31, 2026, and compared it with the pages ranking for this topic. We did not run benchmarks; figures marked Illustrative example are assumptions, not results.
Limitations
AI redlining is weakest on bespoke deals, unusual risk allocation and clauses that depend on commercial context the tool cannot see. Keep those with lawyers.
Pilot results on NDAs do not predict results on complex agreements. Test each contract type you plan to use. Vendor accuracy figures should be treated as claims until you reproduce them.
Our own product (disclosure: LuMay sells this). LuMay's Lexintis legal AI page describes first-pass redlines against your playbook, a clause library, integration with iManage or NetDocuments, and reviewer accept or reject with an audit chain.
Evaluate it with the same checklist. This is not a ranking.
Key Takeaways
• Write the playbook before you pilot any redlining tool
• Score tools on contracts your lawyers have already reviewed
• Require a rule citation and reviewer decision for every edit
• Exclude any tool that fails your confidentiality review
• Pilot each contract type separately before expanding
Next Step
Write or update your playbook, then pilot with contracts your team has already reviewed. See Lexintis contract redlining, and read our guide to redlining third-party contracts with AI and human review.
Sources
• ABA Formal Opinion 512: Generative Artificial Intelligence Tools
• ABA Model Rules of Professional Conduct
• ABA Model Rule 1.6: Confidentiality of Information
• ABA Model Rule 5.3: Responsibilities Regarding Nonlawyer Assistance
