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:
StatementTimeline_eventConflictEvent_evidenceEvent_in_conflict-
Analysis(parent record for each case)
🧠 Two AI Agents:
timeline_extractor(ChatGPT-4o)
→ Parses each statement, extracts events, timestamps, witnessesconflict_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_eventsfor each statement
✅ insert_events
- Calls
timeline_extractor - Saves structured events to
timeline_eventtable
✅ insert_conflicts
- Triggered when user clicks “Detect Conflicts”
- Calls
conflict_detector - Stores results in
ConflictandEvent_in_conflicttables
🖼️ 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
Alex | Sciencx (2025-06-27T09:40:32+00:00) How We Built an AI-Powered Legal Timeline Generator. Retrieved from https://www.scien.cx/2025/06/27/how-we-built-an-ai-powered-legal-timeline-generator/
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