# CargoMesh DockMind

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Axel Arista](https://cerebralvalley.ai/u/Axel_botAfk), [Cristhian Andres Chujutalli Martinez](https://cerebralvalley.ai/u/chujutak), [Juan Antonio Coronado Palacios](https://cerebralvalley.ai/u/Coronao), [JEAN PAUL RAMON FERNANDEZ](https://cerebralvalley.ai/u/jryfark)
- **GitHub:** https://github.com/Jean-RF/cargomesh-meta
- **Demo video:** https://www.youtube.com/watch?v=aFpnR-GYEfE
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4669

**CargoMesh DockMind — Turning Freight Declarations into Verifiable Loading Evidence**

Freight operations often rely on packing lists and declared quantities that are difficult to verify against what is actually loaded. Discrepancies can lead to delays, disputes, manual reconciliation, and limited accountability between shippers and carriers.

CargoMesh DockMind addresses this gap through an AI-assisted, human-reviewed freight verification workflow.

Built during the Meta Global AI Developer Hackathon on top of the existing CargoMesh logistics platform, the project introduces:

- **AI-assisted cargo declaration:** Muse Spark interprets shipment descriptions and supporting evidence, proposing structured cargo information while preserving source attribution and identifying missing measurements.
- **Reviewed shipment manifests:** Users can inspect, correct, confirm, and reopen versioned cargo declarations, including multiple cargo groups.
- **Document evidence management:** Authorized users can associate supporting documents with shipments and review the relevant versions.
- **Loading observation workflow:** Loading sessions connect declared cargo to observations produced by a video-processing pipeline.
- **Reality Passport:** A reviewable record brings together declarations, loading observations, discrepancies, and supporting evidence.

The central design principle is that AI proposes, deterministic business services validate, and humans confirm. The system does not silently change shipment quantities or turn model predictions into confirmed commercial actions.

The prototype uses controlled synthetic freight scenarios and explicitly labeled fixture-based loading evidence to demonstrate reconciliation. These examples are not presented as proof of production-grade physical counting.

**Our goal is to make freight verification more transparent, explainable, and auditable, reducing uncertainty between what was declared and what was observed.**

## More from Meta Global AI Developer Hackathon

- [SN2.0](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4666)
- [Fireshift AI](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4667)
- [Silvi](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4668)
- [khanaitechnologies](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4670)
- [jackpot-joe](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4671)
- [Consai](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/4672)

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