Sentinel 2.0 - Multi-Sensor Fusion
Fuse RGB camera, thermal IR, and LiDAR streams in one edge inference pass for night, fog, and low-contrast detection.
Where edge AI, autonomous systems, and domain-adapted intelligence converge across CGC Sentinel, FleetSim Predictive, Nexus Agents, Cognex RAG, Forge LLM, and new platform layers.
Fuse RGB camera, thermal IR, and LiDAR streams in one edge inference pass for night, fog, and low-contrast detection.
Move from event-driven batch sync to continuous telemetry updates so operators can run what-if scenarios during active missions.
Replace static approval thresholds with Bayesian updates based on resolved incident history and action class risk.
Retrieve engineering diagrams, wiring schematics, and annotated inspection photos alongside text documents.
Run deduplication, PII redaction, domain relevance scoring, and class balance checks before training starts.
Scale multi-agent drone coordination with gossip-based state propagation and sub-100ms leader failover.
A purpose-built edge operating layer that abstracts hardware differences across Jetson, Intel NCS2, Raspberry Pi 5, and ARM SoCs. Edge OS handles model scheduling, thermal management, power modes, OTA orchestration, and secure boot.
Telemetry Workers borrow FleetSim forecasting models to raise pre-fault alerts before component failure.
Capture low-confidence Cognex answers, synthesize fine-tuning data, and trigger scheduled incremental LoRA retraining.
Break complex questions into parallel retrieval threads, MCP calls, synthesis, comparison, and cited recommendations.
Add causal graph models that distinguish true root causes from correlated telemetry symptoms.
Visual odometry and IMU fusion support GPS-denied and comms-denied operations with local replanning.
A unified AI data mesh connecting all five platforms: Sentinel feeds FleetSim, FleetSim triggers Nexus, Nexus creates Forge training data, and Forge improves Cognex answer quality in a closed learning loop.
Multi-organization model collaboration through gradient aggregation without raw data sharing.
Extend tool access into cloud services, Kubernetes clusters, CI/CD pipelines, and OT systems for end-to-end remediation.
Recommend optimal maintenance schedules across cost, mission calendar, parts availability, and fleet risk.
Smart glasses and AR overlays surface maintenance history, open fault codes, and contextual guidance while operators work.
Zero-shot detection from text prompts over drone and IoT imagery using a CGC-specific foundation model.
Event-based cameras and spiking neural networks targeting detection inference at 1-5mW for remote sensors.
Air-gapped, domestically hosted CGC stack for national-scale critical infrastructure and public-sector deployments.
Enable edge models to adapt from new observations locally without data leaving the device.
Long-term goal: autonomous fleet operation with humans involved only for genuine exceptions.
AI that operates at the point of need, including disconnected, regulated, remote, or latency-sensitive environments.
Domain-adapted models trained inside the organization's infrastructure without unnecessary data egress.
Fault detection, self-healing, mission replanning, and human escalation only when necessary.
Privacy by architecture: inference near the source and sensitive data kept local whenever possible.
Operational intelligence for critical infrastructure without dependency on foreign AI platforms.
Detection feeds prediction, prediction feeds agents, agents feed training, and training improves knowledge.