Enterprise IoT & Connected Systems Development

Our comprehensive IoT device engineering encompasses custom hardware design, firmware development, and multi-protocol connectivity optimization. We handle everything from sensor selection and PCB design to power management and enclosure engineering. Each device is designed for reliable operation in real-world conditions with OTA update capabilities and remote diagnostics.

We implement the optimal wireless protocol for your application: WiFi 6 for high-bandwidth applications, BLE 5.x for proximity and wearables, LoRaWAN for long-range low-power deployments, NB-IoT/LTE-M for cellular coverage, Thread/Matter for smart home interoperability, and Zigbee for mesh networking. Protocol selection is optimized for range, power consumption, data rate, and cost.

Enterprise-grade cloud solutions built on AWS IoT Core, Azure IoT Hub, or Google Cloud IoT. We architect scalable backends with time-series databases, real-time stream processing, RESTful APIs, and comprehensive device management. Our solutions handle millions of messages per second while maintaining sub-second latency for real-time applications.

Security is embedded at every layer: hardware security modules (HSM) for key storage, secure boot with certificate verification, TLS 1.3 encrypted communication, mutual authentication, and zero-trust architecture. We implement OWASP IoT security guidelines and industry-specific standards like IEC 62443 for industrial applications and HIPAA for healthcare IoT.

Transform raw IoT data into actionable insights with custom analytics platforms. We build real-time dashboards using Grafana, custom React applications, and mobile apps. Advanced features include anomaly detection using ML models, predictive maintenance algorithms, and automated alerting. Integration with business intelligence tools like PowerBI and Tableau is supported.

Reduce cloud costs and latency by processing data at the edge. Our edge solutions run on devices ranging from microcontrollers to industrial gateways. We implement TensorFlow Lite, Edge Impulse, and custom ML models for on-device inference. Edge computing can reduce bandwidth usage by 70% and enable real-time responses under 10ms.

Combine multiple sensor inputs for enhanced accuracy and reliability. Our sensor fusion algorithms integrate IMUs, environmental sensors, proximity sensors, and application-specific sensors into coherent data streams. Kalman filtering, complementary filters, and ML-based fusion techniques extract maximum value from your sensor array.

Create virtual representations of physical IoT systems for simulation, optimization, and predictive maintenance. Our digital twins synchronize in real-time with deployed devices, enabling what-if scenarios, performance optimization, and predictive failure analysis. Integration with SCADA systems and enterprise resource planning (ERP) solutions is supported.