AI SaaS MVP: Automated Contract Review Platform
An AI-powered contract analysis tool that highlights risky clauses, suggests edits, and compares terms against industry benchmarks for legal teams.
Client: ClauseCheck (Legal tech startup)
Document viewer showing a contract with highlighted clauses in red (risky), yellow (unusual), and green (standard). Right sidebar shows AI analysis with risk score, suggested edits, and benchmark comparison.
The Challenge
Legal teams at mid-market companies spend 4 to 6 hours reviewing each vendor contract. The founder, a former corporate attorney, estimated that 80% of this time was spent on the same 15 clause types: limitation of liability, indemnification, termination, IP assignment, and so on. Junior associates caught obvious issues but missed subtle risks like asymmetric termination rights or uncapped liability. She wanted an AI tool that could do the first pass in minutes, flagging issues for senior review. Existing tools like Kira Systems cost $100K+/year and required months of implementation. She needed something her team could use tomorrow.
Our Approach
The architecture centered on a document processing pipeline. Users uploaded PDF or Word contracts, which we converted to structured text using a combination of PDF parsing and layout analysis. The AI analysis used Claude with a carefully crafted system prompt that defined 15 clause categories, risk levels for each, and industry-standard benchmarks from a curated database. Each clause was scored on a 1-10 risk scale with explanations. The UI was a document viewer inspired by Google Docs: the original contract on the left with colored highlights (red for high risk, yellow for unusual, green for standard), and an analysis panel on the right with detailed findings. We built a clause library of 200+ benchmark terms from publicly available contracts (SEC filings) that the AI used for comparison. The export feature generated a marked-up PDF with all findings that lawyers could attach to their review notes.
What We Built
Delivery Timeline
Day 1-3: Document Pipeline
PDF/DOCX parsing, text extraction, layout analysis, S3 storage, BullMQ processing queue.
Day 4-7: AI Analysis Engine
Claude prompt engineering for 15 clause categories, risk scoring, benchmark comparison logic.
Day 8-11: Document Viewer
Side-by-side UI with colored highlights, analysis panel, clause navigation.
Day 12-14: Benchmark Library
200+ clause templates from SEC filings, comparison algorithm, industry-standard scoring.
Day 15-16: Export + Team Features
Marked-up PDF export, team workspaces, sharing, analysis history.
Day 17-18: Hardening + Launch
Security audit, encryption verification, load testing, production deployment.
Tech Stack
Architecture
frontend
Next.js with custom document viewer component and Tailwind CSS.
backend
Hono on Railway with document processing queue (BullMQ).
auth
Better Auth with team workspaces and SSO readiness.
data
PostgreSQL for users and analysis history. S3 for document storage.
ai
Claude 3.5 Sonnet with structured output for clause-by-clause analysis.
Security
encryption
AES-256 encryption for all uploaded documents. Deleted after 30 days.
rbac
Team workspaces with Admin and Reviewer roles.
compliance
No document data used for AI training. Processing happens in our infrastructure.
monitoring
Audit log for every document upload, analysis, and export.
The Results
“Our associates used to spend all week on contract review. Now they spend 20 minutes reviewing what the AI flagged. We process 3x more contracts with the same team.”
Key Takeaways
Claude excels at legal analysis when given structured output requirements. We defined 15 clause categories with severity rubrics, and the AI matched human reviewers at 96% accuracy.
The benchmark database was the secret weapon. AI analysis without comparison data just says 'this looks risky.' With benchmarks, it says 'this indemnification cap is 3x below industry standard.'
Legal teams care about explainability. Every AI finding includes the exact clause text, the risk reason, and a suggested alternative. Black-box scores get ignored.
Deliverables
FAQ
Frequently Asked Questions
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