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ColdChain Sentinel
ColdChain Sentinel is a synthetic-only, advisory-only cold-chain intelligence platform that helps operators understand what may be going wrong and what a human should inspect first. Instead of treating every temperature excursion as a simple threshold alert, it evaluates data quality, sensor agreement, mapping uncertainty, and risk evidence before presenting a likely fault pattern and an inspection recommendation. Its Sentinel Thermal Behavior Learner (STBL) was trained offline on AMD GPU infrastructure using 171,000 synthetic training windows, 38 project-defined fault classes, and 19 weighted features. The neural model achieved 95.51% synthetic fault accuracy and 99.52% synthetic behavior accuracy. For the live demo, that research was distilled into transparent Python runtime logic, achieving 77.25% synthetic fault accuracy and 94.06% synthetic behavior accuracy without requiring a GPU, PyTorch, notebook, database, or external service at startup. The containerized application includes a command center, end-to-end case walkthroughs, an algorithm evidence console, a fault atlas, inspection guidance, and judge-ready validation evidence. Fireworks AI is available only as an optional, safety-gated explanation layer. Deterministic rules remain authoritative, provider output cannot trigger operational actions, and every recommendation requires human review. The product vision is an evidence and inspection layer for temperature-sensitive logistics. It could help quality, operations, and insurance teams interpret noisy or conflicting sensor evidence faster. The current project uses no real shipment, customer, pharmaceutical, patient, or sensor data. Controlled external validation and data-governance review would be required before any pilot.
13 Jul 2026
