Case study · AI-native · Contract lifecycle management
An AI agent that reviews contracts against a company playbook and drafts the redlines, while people stay in control.
Client branding is removed from all screens. Names and figures in the sample contract are demo data.
Redline Agent is an AI agent inside a contract lifecycle management (CLM) platform. It reviews a contract against the organization's playbook, flags risky or non-standard clauses, and drafts recommended redlines, so legal and business users negotiate faster with less manual effort.
For reviewers, it cuts first-pass review time, keeps redlines consistent with company policy and explains every suggestion. For admins, it gives them a way to define and govern the rules Redline follows, with visibility into how it behaves.
One shared playbook drives every review.
Reviewers read long contracts clause by clause, compared each one to the playbook by memory, and wrote redlines by hand. The same clause could get a different position depending on who reviewed it.
Admins owned the guidance but it lived in documents. They had no reliable way to keep it current or to know whether anyone followed it.
Contract reviewers need a fast, trustworthy first pass that follows company policy, because manual clause-by-clause review is slow, inconsistent and hard for admins to govern.
Show reviewers every risky clause up front, instead of making them hunt for it?
Make each suggested change explain itself, so reviewers can trust or reject it quickly?
Let admins turn existing legal guidelines into rules the agent follows consistently?
Three goals guided the plan: understand how reviews happen today, find where trust in AI suggestions breaks, and learn how playbooks are written and maintained.
| Method | Who | What we wanted to learn |
|---|---|---|
| Stakeholder interviews | Product, legal ops leads | Business goals, success criteria, constraints |
| User interviews | Contract managers, sales reps | Current review steps, workarounds, trust triggers |
| Admin walkthroughs | Playbook admins | How guidelines are authored, updated and enforced |
| Customer survey | Existing CLM customers | How widespread the patterns were |
| Pattern review | Design team | How AI review tools handle suggestions and control |
We spoke with legal ops, contract managers, sales reps and admins. Everyone wanted speed, but reviewers only kept a suggestion when they could see where it came from and change it easily.
"If I can't see which rule it used, I'll redo the review myself."
Contract manager · Illustrative"Our playbook is a long PDF nobody opens mid-review."
Legal ops lead · Illustrative"By round three I've lost track of what we already gave up."
Sales rep · IllustrativeThe first draft had 38 questions. We cut it to 14, then found it no longer covered admins or later negotiation rounds, so we rebuilt it to 26.
Reviewer feedback exposed leading wording and AI jargon. We rewrote questions to ask about current behavior, one idea at a time, in the customers' own words.
How much time would AI-powered redlining save you?
How long does a first review of a standard contract take you today?
How useful are LLM-generated clause recommendations?
When you change a clause, where does the new wording come from?
Is your playbook hard to use and out of date?
When did you last open your team's playbook during a review?
Their needs pull in different directions. Reviewers want less work per contract. Admins want more control over every contract. The design had to serve both without one burdening the other.
The lowest points weren't in negotiation itself. They came from looking things up: the playbook during first review, and past decisions during later rounds.
We compared categories of tools rather than single products. The gaps clustered around explanation, editing before applying, and continuity across negotiation rounds.
| Capability | General AI writing assistants | CLM clause libraries | AI redline tools | Our opportunity |
|---|---|---|---|---|
| Grounded in the company playbook | ||||
| Explains why a change is suggested | ||||
| All flagged clauses in one view | ||||
| Edit a suggestion before applying | ||||
| Amendments and later rounds | ||||
| Refine by conversation | ||||
| Admin guardrails and visibility |
Each insight became a requirement, then a feature. We plotted them by impact and effort with the PM and engineering, and sequenced releases around the top-left quadrant.
Admin work lives in settings; reviewer work lives in the document. The playbook is the only shared object, so its structure (contract type, intent, rules, risk conditions) became the backbone of both experiences.
The first concept walked reviewers through clauses in sequence. It felt guided, but reviewers couldn't see the overall risk of a contract until the end, and couldn't choose where to start.
Redline lives in the full-document panel next to the contract. Reviewers start it from the AI assistant, keep working while it runs, then get every flagged clause ranked by risk with a drafted redline and the reasoning behind it.
Run the agent on a sample contract as a reviewer, or set up playbooks and rules as an admin.
Password protected. Email me at ramprasadp1304@gmail.com for access. Best on a laptop or desktop screen.
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Amendments now show a Parent Agreement Context block, so reviewers judge a change against what was already signed. Subsequent Review groups clauses by what changed since the last round. Historical Precedents synthesizes how similar clauses were settled before.
Sometimes a suggestion is nearly right. Instead of editing legal text by hand, reviewers ask the assistant to adjust it ("make this less strict") or explain it ("why this change?"), and see the document update beside the chat.
Customer shall pay all undisputed invoices within 60 30 days of invoice receipt. A processing fee of 3% shall be added to all payments to cover administrative costs. Late payments shall incur interest of 1.5% per month from the due date. Payment amounts shall be as specified in the applicable Statement of Work.
Admins manage Redline from Solutions setup. Playbooks and rules live in separate libraries, and a guided flow lets AI draft a playbook from an uploaded document, with the admin reviewing every rule before it goes live.
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Six participants completed three tasks: triage a contract's flagged clauses, apply or adjust a suggested redline, and create a playbook from a guidelines document.
| Finding | Severity | Change made |
|---|---|---|
| Reviewers looked for why a clause was flagged before reading the fix | High | Moved risk and rule above the suggestion |
| "Accept all" felt unsafe on high-risk clauses | High | High-risk clauses need a per-clause decision |
| Editing a suggestion wasn't discoverable | Medium | Clause library alternates with one-click Apply |
| Admins didn't understand match percentages | Medium | Named ranges with a recommended default |
| Reviewers lost their place after minimizing analysis | Low | Resume at the first unactioned clause |
Everything used existing PDS components and rules: sentence case buttons, Title Case labels, "New" instead of "Add" or "Create", and brand blue #0170ef. Edge cases and states were documented alongside the screens.
These are targets set with the product team, not measured results. They'll be replaced with live data as usage grows.
Less time on a first-pass review
Suggestions accepted as-is or with small edits
From guidelines document to live playbook