A practical stack for edge inference, event pipelines, predictive analytics, agentic orchestration, grounded retrieval, and model lifecycle management.
Quantized models for low-latency detection on constrained hardware.
Transmit events and evidence instead of continuous raw streams where possible.
Risk scoring, RUL forecasting, route planning, and maintenance optimization.
RAG over manuals, firmware, telemetry, SOPs, and incident records.
Tool-using agents for triage, execution, health checks, and escalation.
Fine-tuning, evaluation, experiment tracking, registry, and edge export.
We do not force everything to the edge or everything to the cloud. The system decides placement by latency, privacy, reliability, compute cost, and observability needs.
Typical deployments keep inference and event creation local, send compact telemetry to central services, and use cloud or serverless GPU workflows for training, analytics, and model evaluation.