An AI-powered litigation support platform for collecting, organizing,processing, analyzing, and reporting digital legal evidence.
Designed for litigation support, family law, civil litigation, internal investigations, eDiscovery, compliance, and digital evidence management.
Legal professionals spend countless hours manually locating, organizing, reviewing, and preparing digital evidence.
This project aims to automate that workflow.
The AI Legal Evidence Processing Platform transforms raw digital evidence into organized, searchable, court-ready evidence through a modular processing pipeline.
Rather than building another file organizer, this project is being engineered as an extensible evidence processing framework capable of supporting multiple evidence sources and future AI-powered analysis.
Clone the repository:
git clone https://github.com/GeoJordan/ai-legal-evidence-processing-platform.git
cd ai-legal-evidence-processing-platformCreate a virtual environment:
python -m venv legal_envActivate it (Windows PowerShell):
.\legal_env\Scripts\Activate.ps1Install dependencies:
pip install -r requirements.txtRun the test suite:
python -m pytestCurrent status:
- ✅ Processing Framework
- ✅ Pipeline
- ✅ Evidence Scanner
- 🚧 Evidence Ingestion Framework (in development)
Run the complete Conversation Intelligence demonstration:
python -m examples.generate_conversation_reportThis demonstrates:
- Evidence Index
- Conversation Builder
- Conversation Analytics
- Conversation Report
Traditional evidence management often requires:
- Manual evidence collection
- Manual email review
- Manual attachment extraction
- Manual exhibit preparation
- Manual timeline creation
- Manual evidence registers
This platform automates these tasks while maintaining a modular architecture that can grow with future capabilities.
- Configuration Engine
- Workflow Controller
- Processing Pipeline
- Processing Framework
- Evidence Context
- Metadata Framework
- Evidence Scanner
- File Discovery
- Modular Processing Stages
- Unit Tested
- Documentation-Driven Development
- Test-Driven Development (TDD)
The AI Legal Evidence Processing Platform includes an integrated demonstration that showcases the complete investigation workflow.
Stages
- Workspace
- Case
- Allegations
- Evidence
- Evidence Selection
- Evidence Gap Analysis
- Discovery Package
- Case Dashboard
- Platform Status
48 automated tests currently pass.
Workflow
│
▼
Pipeline
│
┌──────────────┼──────────────┐
▼ ▼ ▼
Scanner Metadata Future Stage
│ │ │
└──────────────┼──────────────┘
▼
EvidenceContext
The AI Legal Evidence Processing Platform has evolved through several engineering phases:
| Phase | Sprints | Primary Focus |
|---|---|---|
| Phase 1 | 1–3 | Evidence Processing |
| Phase 2 | 4–6 | Evidence Intelligence |
| Phase 3 | 7 | Conversation Intelligence |
| Phase 4 (Current) | 8 | Evidence Intelligence & Case Management |
| Future | 9–10 | Narrative Intelligence & Discovery Automation |
| Sprint | Milestone | Status |
|---|---|---|
| Sprint 1 | Repository Foundation | ✅ |
| Sprint 2 | Configuration & Workflow | ✅ |
| Sprint 3 | Evidence Scanner | ✅ |
| Sprint 4 | Processing Framework | ✅ |
| Sprint 5 | Evidence Ingestion | ✅ |
| Sprint 6 | Evidence Intelligence | ✅ |
| Sprint 7 | Conversation Intelligence | ✅ |
| Sprint 8 | Evidence Intelligence & Case Management | Planned |
| Sprint 9 | Narrative Intelligence | Planned |
| Sprint 10 | Discovery Automation & Court Packages | Planned |
Unlike traditional file organizers, this project is designed as an extensible processing framework.
Every capability—scanning, metadata extraction, OCR, AI classification, timeline generation, exhibit preparation, and reporting—is implemented as a modular processing stage.
This architecture allows new evidence sources and processing capabilities to be added with minimal changes to the core framework.
app/
evidence/
timeline/
models/
configuration.py
context.py
metadata.py
pipeline.py
scanner.py
config/
docs/
examples/
tests/
README.md
requirements.txt
- Python 3.14
- PyTest
- YAML
- Git
- GitHub
- Test-Driven Development (TDD)
- Modular Architecture
| Item | Value |
|---|---|
| Current Version | v0.6.0 |
| Current Sprint | Sprint 7 |
| Current Milestone | Conversation Intelligence |
| Development Status | 🟢 Active Development |
| Architecture | Modular Processing Framework |
| Test Framework | PyTest |
| Language | Python 3.14 |
Sprint 6 introduced the Evidence Intelligence subsystem, enabling intelligent organization, analysis, and reporting of email evidence.
- Evidence Index
- Timeline Generator
- Timeline Report
- Sender Filtering
- Keyword Filtering
- Timeline Statistics
- 48 automated tests
- Architecture Decision Records (ADRs)
- GitHub Release v0.6.0
- 48 automated tests passing
- Test-Driven Development (TDD)
- Modular layered architecture
- Architecture Decision Records (ADR)
- Professional project documentation
Generate a professional evidence timeline report.
python -m examples.generate_timeline_reportExample output:
The next development milestones include:
- Conversation Intelligence
- Email conversation threading
- Relationship graph generation
- AI-powered evidence classification
- OCR document processing
- Duplicate evidence detection
- Exhibit package builder
- Court-ready case assembly
This project was created to explore how software engineering, automation, and artificial intelligence can reduce the manual effort required to process digital legal evidence.
Rather than focusing on a single legal case, the platform is being engineered as a reusable framework capable of supporting multiple evidence sources, extensible processing stages, and future AI-assisted workflows.
Built with Python using a modular, test-driven architecture focused on legal evidence automation.