Factories instrument themselves with sensors, then ignore the data. We close the loop. Predictive maintenance, vision QA, worker safety, digital twin — built for the brutal reality of an industrial floor, where the network drops, the PLCs are 1990s vintage, and the operators have ten seconds to read your screen between rounds.
Vibration ML, FFT signatures, thermal anomaly. Two-week failure-horizon predictions across rotating equipment in 4 industries.
YOLO and segmentation models at the inspection station. EdgeTPU and Jetson deployments. Surface defect classification at line speed.
MQTT brokers, time-series databases, OPC-UA gateways. The plumbing that turns thousands of sensors into actually-queryable data.
PPE detection, intrusion zones, fall-detect wearables. Worker safety dashboards engineered for plant managers, not data scientists.
Unity-based plant replicas wired to live telemetry. Used for training, simulation, and what-if scenario planning by maintenance leads.
NVIDIA Jetson, ARM Cortex-A53, custom industrial PCs. We ship the firmware, the OTA pipeline, and the field-recovery playbook.
Crompton's 4-plant deployment monitors 1,840 induction motors via accelerometers and current sensors. The model — a tuned spectral ConvNet trained on 14,000 historical failures — calls bearing, shaft, and rotor faults a median 14 days before predicted failure. 38% reduction in unplanned downtime across the plants.
Industrial networks fail more than anyone admits. Every edge device buffers locally and reconciles. Every cloud workflow is idempotent. We design for offline-first because the floor doesn't care about your uptime SLA.
The PLC engineer was there before us and will be there after. We meet them in their language — ladder, structured text, OPC-UA tags. We never touch a safety loop.
Dashboards built for plant managers, not data scientists. Six seconds to read. One thumb. Works in PPE gloves. Tested with actual operators before launch.