How to build an AI Contract Analyst

TL;DR

• What it does: Extracts key clauses, checks vs. your playbook, and summarizes risks & next steps.

• Who it’s for: Legal Ops, Procurement, Sales Ops, Finance, founder-led teams.

• Time to value: ~45–60 min setup; 3–7 min per contract after.

• Output: Clause table, deviation report, obligations/calendar, exec summary with citations.

• Fit: Works with SharePoint, Drive, Notion; exports to CSV/JSON and PDF.

Common Pain Points of Reviewing Contracts

• Hunting for renewals, termination, SLAs, and hidden liabilities.

• Inconsistent reviews; playbooks not enforced.

• Missed deviations due to scattered context and version sprawl.

• Manual data entry into CLM/CRM; no structured outputs.

• Limited traceability on who changed what and why.

What the Agent Delivers

• Structured extraction of parties, dates, renewal, liability, IP, DPA, etc.

• Policy check with flagged deviations and suggested fallbacks.

• Risk snapshot with severity scores and deal-blockers.

• Obligations & key dates auto-generated as a calendar.

• Clean exports (PDF, CSV/JSON) plus version diffs with citations.

Step-by-Step Breakdown

1. Files Node (doc-0): Upload & Process Contract

• Purpose: Entry point for users to upload contract files (PDFs, Word docs, or text).

• Action: Extracts raw text from the uploaded file for AI analysis.

2. Extract Metadata & Clauses (llm-0): AI Extraction

• Purpose: Use AI to structure the contract content.

• Extracts:

  
  

  • Contract title

  • Parties involved

  • Effective date

  • Termination date (if present)

  • Key clauses (title + full text)

  
  

• Output: Structured JSON with all extracted fields.

3. Policy Check (knowledgebase-0): Retrieve Company Policy

• Purpose: Search the company’s policy knowledge base.

• Action: Match extracted clauses against relevant policy documents.

• Output: Relevant policy text chunks for compliance checking.

4. Policy Check using Knowledge Base (llm-1): AI Compliance Analysis

• Purpose: Compare contract clauses with policy context.

• Action:

  
  

  • Detects policy violations

  • Suggests recommendations for each violation

  
  

• Output: Structured list of violations and recommendations.

• 
  

5. Risk Summary & Outputs (llm-2): Summarize & Format

• Purpose: Deliver clear results to the user.

• Action:

  
  

  • Summarizes violations and risks

  • Provides actionable recommendations

  • Formats results as a table or JSON

6. Output Node (out-0): Display Results

• Purpose: Show final output.

• Action: Displays results in a user-friendly format (table, text, or structured JSON).

How Data Flows

1. User uploads a contract

2. AI extracts metadata & clauses

3. Clauses are checked against company policy

4. AI analyzes for violations & recommends actions

5. AI summarizes findings

6. User sees results

Node Table

Summary

• Upload contract → AI extracts key info → Check against policy → Identify violations & recommendations → Summarize risks → User sees results

• Fully automated: users only upload a contract. AI + knowledge base handle the rest.