MailIntel AI

Evidence-Driven AI-Powered Email Investigation Platform

MailIntel AI is an enterprise-style email investigation platform designed to analyze RFC822 (.eml) email messages and generate explainable, evidence-driven investigation reports.

The platform combines rule-based analysis, structured evidence collection, explainable risk scoring, and executive reporting into a workflow suitable for cybersecurity investigations, phishing analysis, compliance, and digital forensics.

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Features

• Email parsing (RFC822 / .eml)
• Authentication analysis (SPF, DKIM, DMARC)
• URL and domain intelligence
• Language intelligence
• Evidence collection engine
• Explainable risk scoring
• Executive PDF report generation
• JSON investigation reports
• Interactive Streamlit dashboard
• Docker support
• GitHub Actions CI
• Comprehensive automated tests

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Technology Stack

Category	Technology
Language	Python 3.12+
UI	Streamlit
Validation	Pydantic
Reports	ReportLab
Testing	Pytest
Linting	Ruff
Type Checking	MyPy
Containerization	Docker
CI/CD	GitHub Actions

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Repository

GitHub Repository:

https://github.com/Litap-AI/mailintel-ai

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Quick Start

Clone Repository

git clone https://github.com/Litap-AI/mailintel-ai.git

cd mailintel-ai

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Create Virtual Environment

python -m venv .venv

macOS / Linux

source .venv/bin/activate

Windows

.venv\Scripts\activate

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Install Dependencies

pip install -e ".[dev]"

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Run Application

python -m streamlit run src/mailintel/ui/app.py

Open:

http://localhost:8501

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Docker

Build the application:

docker compose build

Run the application:

docker compose up

Open:

http://localhost:8501

Stop the application:

docker compose down

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Running Tests

Run the complete quality suite:

pre-commit run --all-files

Run unit tests:

pytest

Run static type checking:

mypy src

Run Ruff:

ruff check .
ruff format . --check

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Continuous Integration

Every push and pull request automatically executes:

• Ruff
• Ruff Format
• MyPy
• Pytest

The GitHub Actions workflow ensures all quality checks pass before changes are merged.

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Architecture

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Investigation Workflow

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Project Structure

mailintel-ai/

├── src/
│   └── mailintel/
│
├── tests/
│
├── docs/
│
├── samples/
│
├── Dockerfile
├── docker-compose.yml
├── pyproject.toml
├── requirements.txt
└── README.md

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Core Components

Module	Responsibility
Email Parser	Parses RFC822 email files
Authentication Engine	SPF / DKIM / DMARC analysis
Language Intelligence	Suspicious language detection
URL Intelligence	URL and domain inspection
Evidence Engine	Structured evidence collection
Findings Engine	Rule-based findings
Risk Engine	Explainable risk calculation
Report Builder	JSON investigation report
PDF Generator	Executive investigation report
Streamlit UI	Interactive investigation dashboard

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Investigation Outputs

MailIntel AI generates:

• Executive PDF investigation report

• JSON investigation report

• Evidence collection

• Risk profile

• Explainable recommendations

• Investigation metadata

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Sample Investigation

Input

rawplaintext.eml

Output

• Executive PDF Report
• JSON Investigation Report
• Risk Score
• Evidence Collection
• Recommendations

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Roadmap

Version 1.0

• RFC822 Email Parsing
• Authentication Analysis
• URL Intelligence
• Language Intelligence
• Evidence Collection
• Explainable Risk Engine
• Executive PDF Reports
• Docker Support
• GitHub Actions
• Streamlit Dashboard

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Version 1.1

• Batch Email Analysis
• HTML Reports
• DOCX Reports
• ZIP Upload Support

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Version 2.0

• FastAPI Backend
• React Frontend
• PostgreSQL
• User Authentication
• Investigation Case Management
• Threat Intelligence Integration

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Contributing

Contributions are welcome.

Please read:

• CONTRIBUTING.md
• CODE_OF_CONDUCT.md
• SECURITY.md

before submitting pull requests.

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License

This project is released under the MIT License.

See the LICENSE file for details.

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Author

Rohit Patil

GitHub

https://github.com/Litap-AI

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Acknowledgements

MailIntel AI was developed as a portfolio project demonstrating:

• AI-assisted email investigation
• Explainable risk assessment
• Enterprise software engineering
• Clean Architecture
• Dockerized deployment
• Continuous Integration
• Automated testing