Suri

[su-rì] • verb (Tagalog)

To analyze; examine.

A Real-Time, Privacy-Centric AI Attendance System.
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Installation Guide · Report Bug · Request Feature

Suri is an offline-first attendance solution designed for speed and data privacy. By executing the entire AI pipeline locally, Suri eliminates network latency and keeps biometric data strictly under local control.

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Why Suri?

Local First	Privacy by Default	Remote Sync
AI runs 100% on local hardware. Recognition takes ~15ms because there's no server round-trip.	No biometric data leaves the device unless Sync is enabled. Everything is encrypted at rest.	Backup logs and view reports from any device. End-to-end encryption (E2EE) prevents biometric data from reaching the cloud.

Explore Features →

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Download

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Documentation

Dig into the specifics:

• Features & Capabilities - How attendance groups, sessions, and exports work.
• Architecture & Stack - The Electron + FastAPI + ONNX setup.
• Installation & Setup - Get it running in minutes.
• Troubleshooting - Quick fixes and diagnostic codes.
• Privacy & Security - How we handle biometric data and encryption.

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Tech Stack

Suri uses a modern, high-performance stack to deliver a native experience.

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Roadmap

Phase 1: Local Foundation (Completed)

• Core AI: Local Face Recognition & Liveness Detection.
• Data Management: CSV Import/Export & Group Management.
• Privacy Architecture: Local-first SQLite & E2EE logic.

Phase 2: Connectivity (In-Progress)

• Multi-Camera: Parallel RTSP stream support for large venues.
• Remote Sync: Background synchronization with the Cloud Bridge.

Phase 3: Ecosystem (Future)

• Remote Dashboard: Centralized web-based admin panel for report aggregation.
• Mobile Companion: Attendance check-in and notification app.

Visit the issues page to submit feature requests.

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Contributing

Suri is open source because tools like this should be accessible to everyone. If you want to help make it better, pull requests are always welcome.

1. Fork the project
2. Create a branch (git checkout -b feature/AmazingFeature)
3. Commit what you've built (git commit -m 'Add some AmazingFeature')
4. Push it to your fork (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the AGPL-3.0 License. See LICENSE for more information.

This project relies on open source software. See Third Party Licenses for details.

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Acknowledgments

• FastAPI - Powering the low-latency local API.
• ONNX Runtime - Enabling high-performance edge inference.
• Electron - Providing the native desktop experience.
• React - Modern UI state management.
• OpenCV - The backbone for real-time image processing.

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