How AI Helps Litigators See the Full Picture: An AI Timeline Generator Built With No Code

Introduction: Turning Legal Complexity Into Clarity

In fast-paced litigation, legal teams often find themselves drowning in witness statements, deposition transcripts, and conflicting accounts. Parsing hundreds of pages to reconstruct an accurate event timeline isn’t just tedious—it’s a critical task where human oversight can cost a case.

At Hack the Law Cambridge, Momen participated as a sponsor and mentor, guiding participants in exploring how no-code and AI can tackle these kinds of legal challenges. As part of our workshop, we built a working example: an AI-powered Event Timeline Generator designed to help litigation associates and paralegals automate one of their most repetitive and time-consuming workflows.

[👉 See the full demo here.]

The Legal Pain Point: Manually Rebuilding Timelines

Litigators know the drill: you receive a stack of witness statements, dig through contradictory versions, and try to stitch together a timeline of what actually happened. It’s meticulous, slow, and often done under pressure.

This demo app was built for:

• Litigation associates

• Paralegals

• Legal tech teams looking to automate analysis

By using AI to extract events, detect contradictions, and visualize everything in an interactive timeline, this tool gives legal teams faster insights, better accuracy, and a clear edge in trial preparation.

Why Build This in Momen (and Not Just Use ChatGPT)?

You might be wondering: Can’t I just paste my transcripts into ChatGPT or Gemini and ask for a timeline?

Here’s why that doesn’t cut it for serious legal workflows—and why we built a full app in Momen instead:

1. Tailored Workflows Beat One-Off Prompts

LLMs like GPT and Gemini are great for generating text—but they lack structure, repeatability, and reliability.

The timeline tool built with Momen guides users through a structured process:

• Ingest statements from multiple witnesses

• Trigger consistent AI analysis

• Automatically visualize contradictions

This is not a one-off prompt — it’s a repeatable, reliable workflow that anyone on the legal team can use.

2. Storage and Retrieval Matter

In real legal cases, you don’t just need insights — you need a system of record. In this project built with Momen, you can have:

• A structured database of every event and quote

• The ability to edit, export, and revisit cases

LLMs alone don’t offer persistent memory or internal collaboration features. An app built on Momen does.

3. User-Friendly, Not Engineer-Only

Instead of writing complex prompts or instructions every time, users just click buttons, a visualized timeline will be then generated, and the conflicts will be detected.

It’s designed for litigation associates and paralegals, not prompt engineers.

4. Multi-Modal Capabilities with Gemini 2.5

Momen supports advanced models like Gemini 2.5, which can analyze:

• Text

• Images

• Even videos

This opens up powerful future use cases when it involves depositions videos or CCTV clips.

This isn’t just a chatbot — it’s a domain-specific AI application that brings structure, persistence, and scale to a high-stakes legal workflow.

What the App Can Do: Core Features

Key Features:

• Raw Text/Video Ingestion: Upload or paste unstructured text or upload videos from witness statements.

• AI-Powered Timeline Generation: Automatically extracts key events and timestamps using Gemini 2.5.

• Conflict Detection: Highlights inconsistent testimonies side-by-side.

• Interactive Timeline Viewer: Explore key moments with exact quotes and references.

• Structured Data Storage: Events are saved in a structured format in the database for later use.

Under the Hood: How It’s Built with Momen

Structuring the Data

We created six interconnected tables for this project.

• Statement (raw input from the user)

• Analysis(an id that connects everything from one analysis)

• Timeline_event (structured timeline data)

• Event_evidence(the evidence from the original quotes)

• Conflict(flagged inconsistencies between statements)

• Event_in_conflict(events in timelines that show conflicts)

The AI-Powered Backend

Two AI Agents, Two Specialized Roles

Two AI agents do the heavy lifting, triggered by Actionflows (backend workflow).

1. timeline_extractor (ChatGPT-4o)

This agent processes each statement, extracts events, timestamps, and witness names, outputed in a structured format.

2. conflict_detector (Gemini 2.5)

Compares statements and flags discrepancies—then links each to its timeline event.

Tech Highlight: By using two different AI models for separate tasks, we optimized performance: ChatGPT-4o for structured parsing, and Gemini 2.5 for cross-statement comparison.

Actionflows That Orchestrate AI

We built two main Actionflows:

• Events extractor

When the user clicks the "Generate Timeline" button:

1. Create an Analysis ID A new entry is inserted into the Analysis table. This ID groups all statements and results for one case.

2. Insert Statements The two input fields are saved as separate records in the Statement table — both linked to the same analysis_id.

3. Trigger process_statements Actionflow This Actionflow:

   1. Retrieves all statements linked to the current analysis_id

   2. For each statement:

      • Calls the insert_events Actionflow

4. Trigger insert_events Actionflow (One per Statement) For each statement:

   1. Calls the timeline_extractor AI agent (powered by ChatGPT-4o)

   2. AI parses the content and returns a list of structured timeline events:

      • Witness name

      • Event description

      • Timestamp or time reference

   3. These events are saved in the timeline_event table, each linked back to the original statement and the current analysis.

📌 Technical Highlight: This multi-step flow automates AI processing for multiple inputs and stores the results in real time — without writing a single line of code.

• Conflicts detector

  

  When the user clicks the "Detect Conflicts" button:

  1. Trigger insert_conflicts Actionflow This Actionflow:

     1. Collects all events from the current analysis

     2. Passes them to the conflict_detector AI agent (powered by Gemini 2.5)

     3. The agent analyzes overlapping timelines and flags conflicting events

  2. Format & Store Conflict Data A custom code block:

     1. Parses the AI response

     2. Inserts new records into the Conflict table and Event_in_conflict table

     3. Links each conflict to the relevant timeline events and statements

  3. Trigger UI Update in Real Time Because the frontend List Component is subscribed to the timeline_event table:

     1. Events with conflicts are immediately updated

     2. The timeline shows red markers for conflicting points

     3. Related quotes and evidence appear in the sidebar

  📌 Technical Highlight: By combining Gemini’s advanced reasoning with Momen’s structured data handling, the app provides real-time legal insights with complete traceability.

  How the Frontend Talks to the Backend

  Every user interaction — from entering a statement to detecting conflicts — is tied directly to backend Actionflows. Here’s how it works:

  Page 1: Submit & Generate Timeline

  • Input Fields

    Two simple text inputs for witness statements.

  • "Generate Timeline" Button

    This button kicks off the entire backend chain:

    • Triggers an insert to the Analysis table to create a new session.

    • Uses a batch mutation to insert both statements into the Statement table.

    • Fires the process_statements Actionflow, which runs AI analysis and stores structured timeline events.

      

      

  Page 2: Interactive Timeline Viewer

  • Timeline List View

    Built with Momen’s List component, this view shows:

    • Each timeline event

    • Its source quote or evidence

    • Real-time updates (subscribed to timeline_event table)

      

  • Conflict Visualization

    • Events flagged by the conflict detector turn red

    • The right sidebar displays conflict-specific quotes and who said what

  • "Detect Conflicts" Button

    Triggers the insert_conflicts Actionflow, which:

    • Calls Gemini 2.5 via the conflict_detector agent

    • Updates the database with conflict metadata

    • Instantly reflects changes in the UI via conditional views

      

      

Results: What This Demo Achieved

This project was completed in just two days during a hackathon setting. Despite the tight timeframe, it was able to demonstrate real-time processing of multiple pages of legal statements and extract a structured timeline in under 30 seconds.

In practical terms, that means a legal associate or paralegal could go from raw, unstructured text to a clear, conflict-highlighted event timeline in less than a minute—dramatically reducing the manual workload and minimizing the risk of missing key discrepancies.

The total cost to build this prototype using Momen was approximately $99. This included two AI agents using different LLM models (ChatGPT-4o and Gemini 2.5), as well as storage, UI, and backend logic. For a team that needs to build internal tools or domain-specific SaaS products quickly. While the demo focused on legal use cases, the same approach could easily apply in fields like healthcare, insurance, or automotive services, where accuracy, traceability, and speed of analysis are equally critical.

What’s Next: Expanding the Use Case

While this demo focused on processing text-based statements from witnesses, the underlying framework supports broader applications—particularly in domains where timelines and inconsistencies matter.

Because the system was built with Gemini 2.5, which supports multi-modal inputs, it can potentially be extended to analyze visual content like deposition videos or CCTV footage. For example, a future version might take in both a video file and a transcript, identify key moments in both, and merge them into a unified timeline with supporting evidence from multiple sources.

Additionally, the structured database model means it could integrate with case management systems or document repositories, allowing legal teams to maintain continuity across matters. Over time, such tools could evolve into lightweight internal platforms that augment—not replace—legal review processes by flagging contradictions early and giving analysts more context in less time.

This type of project doesn’t aim to automate judgment but to support it—making the case-building process more transparent, less error-prone, and ultimately more effective.

Want to know more details? Check how this project is built:
https://editor.momen.app/tool/RZJyj4e4dQ8/WEB?code=qh9Tg7XU9wtRp

FAQ

What problem does the Event Timeline Generator solve?

It automates the manual process of reading through multiple witness statements or transcripts to build a factual timeline and identify contradictions. This helps litigation teams work faster and avoid missing key inconsistencies in testimonies.

Who is this tool designed for?

It’s designed for litigation associates, paralegals, and legal professionals who need structured insights from large volumes of unstructured text—without writing code or managing AI infrastructure.

Why not just use ChatGPT or Gemini directly?

While LLMs are powerful, legal workflows need structure, consistency, and memory. Momen provides a repeatable process: you can upload multiple statements, generate timelines, detect conflicts, and store results in a structured, searchable format—all without writing prompts every time.

What AI models were used in the demo?

The app uses two specialized AI agents:

• timeline_extractor powered by ChatGPT-4o, which extracts events and timestamps.

• conflict_detector powered by Gemini 2.5, which compares statements to find contradictions.

How was the app built without writing code?

The entire app was built using Momen’s no-code platform, which includes visual database modeling, backend workflows (Actionflows), frontend logic, and AI integration. Buttons, inputs, and AI actions are all configured visually.

Can this handle videos or images too?

Yes, Momen supports multi-modal models like Gemini 2.5, which opens the door to processing video evidence like CCTV or deposition footage in future versions.

How long did the project take to build?

The demo was built in two days during a hackathon. Despite the short timeline, it successfully handled real-time processing of multiple pages of statements.

Is this a production-ready app?

Yes, once you click "publish" in Momen, your app is live and production-ready.