Digital accident surveying and real-time AI analysis
A hybrid architecture that closes the gap between field data collection and downstream system availability, integrated with an on-premise AI call-analysis pipeline.
Traditional accident surveying relies on paper-based field collection and manual office transcription. Every transcription is an error point; every hour of latency is an interval in which data is unavailable to downstream systems. Objective: equip the call-centre operator with proactive contextual support — transcription, semantic understanding, and operational suggestions — during the call itself.
Technical traits
Edge (field tablet): Laser BLE, NFC, GPS, Camera, AES signature, AES-256-encrypted local store, Outbox pattern. Backend: multi-tenant JWT gateway, central archive. On-premise AI pipeline: STT, prosodic analysis, NER, LLM. Real-time operator dashboard via WebSocket. No audio transmitted to external infrastructure.
Rugged offline-first tablet
Direct acquisition from measurement instruments, AES-256-encrypted local store, Outbox pattern with guaranteed-delivery synchronization.
Digital accident report with structural checks
Georeferenced photographs with a tamper-proof timestamp. Real-time policy verification. Digital signature with a one-time code. The generated PDF carries a cryptographic fingerprint.
Real-time on-premise AI pipeline
Five parallel models: transcription, prosodic analysis, entity extraction, semantic analysis, and proactive suggestions for the operator.
The architecture eliminates the manual transcription phase from the surveying process. During the call, the centre operator has access to real-time transcription, continuous emotional-state assessment, real-time semantic analysis, and proactive contextual suggestions.