IoT Development from Firmware to Cloud Dashboard

We build industrial monitoring systems (vibration, temperature, pressure), smart building solutions (occupancy, HVAC, energy), consumer connected products, agricultural sensor networks, cold-chain logistics trackers, and custom device integrations for existing hardware. Every project starts with a connectivity and power constraints review.

Yes. Our embedded engineers write firmware in C, C++, and MicroPython for ESP32, STM32, Raspberry Pi, and Arduino-class hardware. We implement low-power sleep cycles, sensor drivers, and OTA update systems. We also partner with hardware design firms when a custom PCB is part of the scope.

We work with MQTT, HTTPS, CoAP, Modbus, BACnet, AMQP, and LoRaWAN depending on the use case. For cellular deployments we use SIM7600 and Quectel modules with NB-IoT or LTE-M. Protocol selection is driven by message frequency, payload size, power budget, and network availability at the deployment site.

Security is layered at every level: TLS 1.3 for transport, X.509 device certificates provisioned in secure element storage, least-privilege IAM policies on the cloud broker, encrypted firmware images for OTA, and network segmentation recommendations for on-premise deployments. We conduct a threat model review at the start of every engagement.

We implement local buffering on the device so data is persisted to flash when connectivity drops and flushed in batches on reconnection. For critical alerts we use SMS or LoRaWAN as a secondary channel. The cloud backend handles out-of-order message arrival and duplicate detection transparently.

Yes. We integrate IoT data streams with SAP, Oracle, Ignition SCADA, and building management systems via OPC-UA, REST APIs, or database connectors. We normalise units and timestamps before writing to any downstream system to prevent data quality issues.

We primarily use AWS IoT Core, Azure IoT Hub, and Google Cloud IoT (Pub/Sub). For on-premise or private cloud deployments we use Eclipse Mosquitto or HiveMQ. Database choices depend on query patterns: InfluxDB for pure time-series, TimescaleDB for relational time-series, and Postgres for configuration and metadata.

We design for horizontal scale from the start: managed MQTT brokers with auto-scaling groups, partitioned Kinesis or Kafka streams for high-throughput ingestion, and pre-aggregated rollups so dashboards do not hit raw data tables. We load-test the architecture against your projected device count before production launch.

Yes. We deploy quantised ML models (TensorFlow Lite, ONNX) onto microcontrollers and edge compute modules (NVIDIA Jetson, Coral TPU) for on-device inference. Common use cases include anomaly detection, predictive maintenance, and computer vision quality control - all running locally without cloud round-trips.

A cloud dashboard for an existing hardware device takes 6-10 weeks. A full-stack system including firmware, connectivity, cloud backend, and dashboard for a new device takes 4-8 months. Hardware procurement and regulatory certification (FCC, CE) are external dependencies that can add 2-4 months.

A cloud integration for existing hardware starts at $20,000-$45,000. For a full end-to-end system the software half - both clients on one backend with the whole module list switched on - is 2,200-4,300 hours, or $67,000-$131,000. Firmware, hardware and regulatory certification are quoted separately, because they are not software hours. Ongoing cloud spend is billed by your cloud provider and scales with message volume and data retention. Price the software half at kultrix.com/cost-calculator.

Our fleet management system maintains a heartbeat timestamp for every device and raises a configurable alert when a device misses its expected check-in. Alerts can route to email, Slack, PagerDuty, or a custom webhook. The dashboard shows last-seen timestamps and accumulated offline duration for every device in your fleet.