Sentinel
Real-time AI decision system that turns raw, continuous inputs into clear, actionable operator guidance.
The System
A four-stage pipeline that processes continuous data streams and delivers operator-ready guidance in under one second.
Raw streams enter through INGEST, where they are normalized and time-aligned. INTERPRET runs event detection and anomaly classification. REASON synthesizes cross-stream context through an LLM layer. OUTPUT delivers prioritized, confidence-scored actions to the operator.
What It Does
Key performance metrics and core capabilities of the deployed system.
- //Detects anomalies in live video/audio/sensor streams
- //Classifies events with confidence scoring and risk assessment
- //Outputs prioritized operator actions in sub-second latency
- //Recommends next-best-action under uncertainty
Key Decisions
Architecture choices that shaped the system and the tradeoffs behind each one.
Streaming Over Batch
Built the entire pipeline around streaming primitives for sub-second latency from ingest to operator output.
↳ Tradeoff: Higher infra complexity for real-time capability.
LLM as Reasoning Layer
Uses an LLM to synthesize cross-stream context and generate operator guidance, not just classify inputs.
↳ Tradeoff: Token cost vs. depth of analysis.
Zero-Noise UI
Operator interface surfaces only actionable items. Everything below the confidence threshold is suppressed.
↳ Tradeoff: Less data on screen, but faster decisions.
Confidence Scoring
Every output includes a confidence score and risk level. Operators see exactly how certain the system is.
↳ Tradeoff: Slower inference, but operator trust goes up.