How We Built an AI-Powered Legal Timeline Generator

This content originally appeared on DEV Community and was authored by Alex

TL;DR: At Hack the Law Cambridge, we built an AI-powered Event Timeline Generator using Momen, ChatGPT-4o, and Gemini 2.5—no code required. It extracts events from legal text, detects conflicts between witness statements, and visualizes everything in a timeline. This post breaks down the full architecture so you can reverse engineer it yourself.

The Legal Pain Point: Reconstructing Timelines from Testimonies

Litigation teams often spend hours parsing through witness statements, deposition transcripts, and contradictory accounts to rebuild a factual timeline. It’s manual, high-stakes, and prone to human error.

So we asked: Can we automate this?

The Tool: AI-Powered Timeline Generator (Demo App)

We built this app for:

• Litigation associates
• Paralegals
• Legal tech teams

Core Features:

• Upload or paste raw text (statements or transcripts)
• AI extracts events and timestamps
• AI detects and highlights contradictions
• Interactive timeline view with source quotes
• Structured database for case history

Why Not Just Use ChatGPT?

Sure, you can paste text into ChatGPT—but legal workflows need:

• Structure
• Repeatability
• Traceability
• Collaboration

That’s why we used Momen, a no-code platform where you can build full-stack apps with backend logic, UI, databases, and AI agents.

How It Works (Full Stack Breakdown)

Database Schema

We created 6 interrelated tables:

• Statement
• Timeline_event
• Conflict
• Event_evidence
• Event_in_conflict
• Analysis (parent record for each case)

Two AI Agents:

1. timeline_extractor (ChatGPT-4o)
   
   → Parses each statement, extracts events, timestamps, witnesses

2. conflict_detector (Gemini 2.5)
   
   → Compares statements and flags conflicting events

Actionflows = No-Code Backend Logic

process_statements

• Triggered when user clicks “Generate Timeline”
• Creates an analysis_id
• Saves input statements
• Calls insert_events for each statement

insert_events

• Calls timeline_extractor
• Saves structured events to timeline_event table

insert_conflicts

• Triggered when user clicks “Detect Conflicts”
• Calls conflict_detector
• Stores results in Conflict and Event_in_conflict tables

Frontend (Also No Code)

Built using Momen’s visual UI components:

• Text inputs for witness statements
• Button to trigger timeline generation
• Interactive timeline list view (subscribed to timeline_event)
• Sidebar to show conflict details

Conflicting events show in red, with side-by-side quote comparison.

Timeline: Built in 2 Days

Even under hackathon time pressure, the app:

• Parsed multi-page legal text
• Built a timeline in under 30 seconds
• Flagged conflicts with full traceability

Total cost? ~$99 (includes both LLM agents + infra).

Want to Reverse Engineer It?

You can explore the full setup—database, agents, and Actionflows—here:

Reverse engineer the full Momen build (view-only link)

Yes, you can view every detail of how it works.

For a more detailed breakdown, check it here

What’s Next?

Because it’s built with Gemini 2.5 (multi-modal), future versions could:

• Analyze deposition videos or CCTV
• Merge transcripts + video into one timeline
• Integrate with document management systems

Drop questions or ideas in the comments!

This content originally appeared on DEV Community and was authored by Alex