SmartEspIoT deploys AI models directly on edge devices for real-time sensor fusion, anomaly detection, and predictive maintenance — no cloud round-trips, no latency, no compromise.
Our platform brings ML inference to the sensor — fusing streams, detecting anomalies, and triggering actions in milliseconds.
Combine heterogeneous sensor streams — vibration, temperature, pressure, vision, acoustic — into unified state representations. TensorRT-accelerated fusion nets run at 200+ Hz on Jetson-class hardware.
Unsupervised temporal models learn normal operational baselines per device. Detect drift, degradation, and failures before they happen with 99.2% precision and sub-5ms inference latency.
Forecast remaining useful life (RUL) for rotating equipment, batteries, and actuators. Survival analysis models trigger maintenance windows that cut unplanned downtime by 73%.
Push model updates to 50,000+ edge nodes with rollback guarantees. Delta-based OTA updates, A/B model deployment, and per-device policy configuration via a single control plane.
End-to-end TLS, hardware-rooted attestation, and on-device key rotation. Models encrypted at rest. Zero-trust mesh networking between edge nodes. SOC 2 Type II ready.
Native connectors for MQTT, Modbus, OPC-UA, CAN bus, LoRaWAN, BLE, and raw serial. Drag-and-drop protocol binding — no code changes when swapping sensor vendors.
From sensor to action — a complete pipeline that runs without cloud round-trips.
Model training, fleet management, analytics dashboard. Async aggregation of edge telemetry. No real-time dependency.
NVIDIA Jetson Orin Nano / Xavier NX gateways run TensorRT-optimized inference. Aggregates local sensor mesh, executes anomaly models, maintains local state. Operates fully offline.
ESP32 / nRF52 / STM32 nodes with TinyML models for first-pass filtering. Wake-on-event, ultra-low-power. Mesh networking with self-healing topology.
Real-world deployments running on production edge fleets.
Vibration + temperature sensors on 12,000 motors across 3 plants. Anomaly models detect bearing degradation 2-4 weeks before failure. Saved $4.2M in unplanned downtime in year one.
8,500 panel-level sensors tracking irradiance, temperature, and output. ML models detect panel soiling, string faults, and inverter degradation in real-time. 4.1% yield improvement.
Refrigerated truck fleets with ESP32 sensor nodes logging temperature every 30 seconds. Predictive models forecast compressor failures 72 hours ahead. Zero spoilage events since deployment.
1,200-building portfolio with occupancy, CO2, and thermal sensors. Reinforcement learning agents optimize HVAC setpoints per zone. 31% energy reduction across portfolio.
Join industrial teams using SmartEspIoT to cut downtime, optimize operations, and unlock real-time AI at the sensor level.