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Welcome to the official documentation for MailIntel AI, an evidence-driven AI-powered email investigation platform designed to analyze RFC822 (.eml) email messages and generate explainable investigation reports.
MailIntel AI assists cybersecurity professionals, researchers, and analysts by transforming raw email messages into structured investigation reports.
The platform performs:
- RFC822 Email Parsing
- Authentication Analysis (SPF, DKIM, DMARC)
- URL Intelligence
- Language Intelligence
- Evidence Collection
- Explainable Risk Scoring
- Executive PDF Report Generation
- JSON Investigation Reports
The objective is to provide investigators with transparent, explainable, and evidence-backed results rather than opaque risk scores.
- AI-assisted email investigation
- Explainable evidence collection
- Rule-based findings engine
- Executive PDF reporting
- Interactive Streamlit dashboard
- Dockerized deployment
- GitHub Actions CI
- Automated testing with Pytest
- Type-safe implementation using MyPy
- Clean Architecture
MailIntel AI is designed for:
- Cybersecurity Analysts
- SOC Teams
- Digital Forensics Investigators
- Security Researchers
- Students
- Developers building security tools
The Wiki is organized into the following sections:
| Page | Description |
|---|---|
| Architecture | System architecture and design principles |
| Investigation Workflow | End-to-end investigation process |
| Installation Guide | Local and Docker installation |
| Developer Guide | Repository structure and development workflow |
| Threat Model | Security assumptions and limitations |
| FAQ | Frequently asked questions |
| Roadmap | Planned future development |
| Component | Technology |
|---|---|
| Language | Python 3.13+ |
| User Interface | Streamlit |
| Validation | Pydantic |
| PDF Reports | ReportLab |
| Testing | Pytest |
| Static Analysis | Ruff |
| Type Checking | MyPy |
| Containerization | Docker |
| Continuous Integration | GitHub Actions |
The project focuses on three engineering principles:
Every investigation result should be traceable back to collected evidence.
The application should produce consistent investigation results for the same input.
The codebase follows Clean Architecture principles to simplify future enhancements and long-term maintenance.
GitHub Repository
https://github.com/Litap-AI/mailintel-ai
MailIntel AI is released under the MIT License.