Legal playbook software: how AI checks contracts against your playbook

Below the guide you will find the original workflow card from my AI agent database, with the agent steps, guardrails and risks.

What is legal playbook software?

A contract playbook is the rulebook for one contract type. Harvey describes it as "the positions your team prefers, the fallbacks it will accept, and the point where a deviation goes to a lawyer". Legal playbook software stores those rules in a structured form and runs them on a draft, usually counterparty paper.

It is a different thing from a clause library. A clause library stores approved wording for reuse. A playbook tells the reviewer how to judge wording the other side already proposed (Harvey).

What does one playbook rule contain?

The vendors use different names, but a rule has roughly the same parts. Spellbook, for example, lets you attach a fallback position, a risk level, a reviewer note, preferred language, a suggested comment and a required approver to each rule.

The payment example comes from Harvey's own write-up (prefers 30 days, accepts 60, a "Net 75" draft lands). Your numbers will be different.

How does an AI playbook review work, step by step?

1. Intake. A third-party agreement arrives, usually as a Word file in the legal queue or attached to a procurement or sales request.
2. Clause detection. The tool finds every clause a rule covers. Ironclad first looks for exact matches of your preferred and fallback wording, then uses an AI clause model if nothing matches.
3. Position check. Each covered clause is sorted into acceptable, needs review, or unacceptable.
4. Uncovered clauses. Clauses that no rule covers get flagged for manual review instead of being passed silently. Harvey documents this behavior.
5. Redline draft. For anything out of range the tool proposes fallback or preferred language as tracked changes, which the reviewer accepts or rejects in Word.
6. Approval and routing. Non-standard terms go to the named approver. In Ironclad that can be finance for payment terms, so legal does not have to clear every exception itself.
7. Feedback into the playbook. If the same fallback gets accepted deal after deal, it may be your real standard. Harvey suggests reviewing rules on exactly that signal.

The legal judgment stays with the lawyer in steps 3 to 6. The software does the first read and points at every covered clause, so the lawyer starts from a list of exceptions instead of a blank draft.

What do the tools on the market already do?

Only what each vendor documents publicly, checked on 21 October 2026. Features change often, so verify in a demo.

What should you test before buying legal playbook software?

• Run it on contracts you already negotiated. You know the right answer for each clause, so you can count misses and false "acceptable" results.
• Check what it does with clauses outside the playbook. A tool that says nothing about an uncovered clause is riskier than one that flags it.
• Check the reason behind each flag. Every flag should point to the rule and the exact contract text it relied on.
• Find out where the documents go. Ask where contracts are processed and stored, who at the vendor can access them, and whether they are used for training. ABA Formal Opinion 512 asks lawyers to assess exactly these confidentiality risks before putting client information into a generative AI tool.
• Test it in your language, jurisdiction and contract types. Ready-made playbooks cover specific contract types and positions. LegalOn, for example, lists separate international playbooks for cross-border deals, with MSA and NDA templates for 20+ jurisdictions.
• Look at the approval trail. You should see who accepted which redline and why, after the fact.

What are the limits?

• AI tools still make things up. Stanford RegLab and HAI tested legal research tools from LexisNexis and Thomson Reuters in 2024. Even these tools, built on retrieval, gave incorrect or misgrounded answers in more than 17% of queries, and one tool in more than 34%. That study covered legal research, not playbook review, but the failure mode is the same: a confident answer citing a source that does not support it.
• The playbook decides the quality. The software applies rules you wrote. Missing, vague or outdated rules produce missing, vague or outdated flags.
• A lawyer still signs off. ABA Formal Opinion 512 says lawyers must understand the benefits and risks of the tool, apply an appropriate degree of independent verification to its output, and not rely on it alone for tasks that call for professional judgment. The workflow card below rates this workflow as high risk and complexity 5 out of 5 for the same reason.
• The time saved depends on your baseline. Vendor figures describe their own customers. Measure first-pass review time on your own contracts before and after a pilot.

When does a custom playbook agent make sense?

For most in-house teams reviewing English-language contracts in Word, buying one of the tools above is the faster route. Building your own starts to make sense when:

• contracts arrive through your own system (a procurement portal, a CRM, a client app) and the review result has to go back there,
• the rules depend on data the legal tools cannot see, such as vendor risk scores, budget approvals or customer tier,
• you work in a language or jurisdiction the vendor playbooks do not cover well,
• you want the review step inside a product you sell, for example a contract feature for your own customers.

A custom agent follows the same loop as the steps above: parse the document, detect clauses, compare each one to structured rules, draft a cited redline, and stop for human approval before anything is sent. Public datasets help with testing. CUAD from The Atticus Project, for example, has expert labels for 41 clause categories across 510 commercial contracts.

Where I can help

I am Bogumił Łuć, a solo AI product builder from Kraków. I build AI agents, web and mobile apps, and MVPs. I am not a lawyer: the legal positions come from your team, and I build the software that applies them, keeps the evidence and stops for approval.

We start with a workshop to pick one contract type and one review path, then build in 2-week sprints, with a clickable version as early as possible. Billing is hourly on a B2B invoice, and I reply within 24 hours. See AI agent development or all services.

FAQ

Sources

• Harvey: Run Your Contract Playbook on Every Draft, Then Refine the Rules
• Ironclad Help Center: Ironclad AI Playbooks Overview
• Ironclad Help Center: Ironclad Jurist for Microsoft Word
• LegalOn: Contract Review Playbooks
• Spellbook Help Center: Create Playbooks
• Stanford HAI: AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries
• arXiv (Stanford RegLab): Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
• American Bar Association: ABA issues first ethics guidance on a lawyer's use of AI tools (Formal Opinion 512)
• American Bar Association (copy hosted by ACC): Formal Opinion 512: Generative Artificial Intelligence Tools
• LegalOn: Contract Review AI in Microsoft Word
• arXiv (The Atticus Project): CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review