Services / Internet of Things
Internet of Things
Intelligence at the edge - from sensor data to autonomous action
Overview
The Internet of Things generates the raw material of operational intelligence - sensor readings, telemetry streams, machine states, environmental data. But raw IoT data alone creates noise, not insight. NINtec's IoT practice delivers the full stack: sensor integration, real-time data pipelines, edge AI inference, and the ML models that turn telemetry into predictive action.
The practice serves two primary use cases: Industrial IoT for manufacturing and automotive OEMs, and Connected Platform IoT for logistics, retail, and smart building applications.
Edge AI is the architectural pattern that separates NINtec's IoT work from commodity implementations. Running inference at the edge reduces latency from seconds to milliseconds and reduces cloud bandwidth costs by 60-80%.
Why NINtec
- AI-first engineering methodology
- 30+ Fortune 500 clients
- 452 engineers across 30 countries
- Fully self-funded growth
Capabilities
How We Use AI
Claude generates IoT data pipeline code from sensor specifications.
AI trains computer vision models on factory-floor defect image datasets.
Claude designs MQTT topic hierarchies and data schema for large device fleets.
AI continuously monitors edge model accuracy and flags drift for retraining.
Technology Stack
Edge
Raspberry Pi, NVIDIA Jetson, AWS Greengrass, Azure IoT Edge
Protocols
MQTT, OPC-UA, Modbus, CoAP, HTTP/REST
Platforms
AWS IoT Core, Azure IoT Hub, Google Cloud IoT
CV
PyTorch, TensorFlow, OpenCV, YOLO models
Digital Twin
Azure Digital Twins, custom simulation frameworks
Time-Series
InfluxDB, TimescaleDB, AWS Timestream
Case Study
European Automotive OEM: 67% defect reduction through IoT + computer vision quality platform. 3 production lines instrumented with 240 sensors and 12 vision cameras. ML models predict defect probability with 94% accuracy.
Full case study