IoT spans hardware, firmware, connectivity, cloud, data, and application. We cover all of it - from architecture and embedded development to dashboards and analytics.
Before any development begins, we design the right architecture for your use case - device selection, communication protocols, cloud platform, data models, and security framework.
→ Strong early decisions prevent months of rework
Common Use Cases
Custom firmware for microcontrollers and embedded Linux systems using C, C++, and Rust. Low-power optimisation, OTA update mechanisms, watchdog recovery, and hardware abstraction layers.
→ Production-grade device reliability at the edge
Common Use Cases
Backend systems that ingest, process, store, and act on data from thousands of connected devices - on AWS IoT Core, Azure IoT Hub, Google Cloud IoT, or a custom-built platform.
→ Scales from pilot to thousands of devices
Common Use Cases
Integration across MQTT, CoAP, AMQP, HTTP/S, Modbus, BACnet, OPC-UA, BLE, Zigbee, Z-Wave, LoRaWAN, NB-IoT, and LTE-M with gateway and edge-to-cloud architecture.
→ Connects heterogeneous devices reliably
Common Use Cases
On-device intelligence for latency-sensitive or bandwidth-constrained applications using TensorFlow Lite, ONNX, and OpenVINO - so critical decisions don't depend on cloud round-trips.
→ Decisions happen locally when latency matters
Common Use Cases
Web and mobile dashboards that give operators, managers, and executives live visibility into device status, sensor readings, alerts, and trends.
→ Operational visibility for the people who need it
Common Use Cases
IoT projects fail at a higher rate than standard software projects. Here's what we do differently - and why it matters for your team long-term.
The firmware engineer knows what the cloud architect is building. The data pipeline team understands the sensor sampling rates. IoT failures usually happen at the boundaries between layers - we prevent them by keeping the full picture in mind throughout.
A PoC that works in a lab is not the same as a system that handles 10,000 devices, intermittent connectivity, firmware update failures, and malformed data packets at 3am. We design for those conditions from the start.
Connected devices that aren't secured properly become attack vectors. We implement device identity provisioning, mutual TLS, firmware signing, secure OTA updates, and network micro-segmentation from the first sprint.
Your devices will lose connectivity. Batteries will die. Sensors will return out-of-range values. We design edge caching, store-and-forward mechanisms, anomaly detection, and alert logic for how physical systems actually behave.
Hardware supply chains, certification requirements (CE, FCC, BIS), lead times, and component availability are real constraints. We surface these early, recommend proven components, and build procurement timelines into our project plans.
We recommend managed IoT platforms where they make sense and custom infrastructure where proprietary control or cost justify it. We'll always explain the trade-off clearly and give you the information to make that call yourself.
IoT projects involve more moving parts than most software projects. Our process reduces risk at each stage before moving to the next - especially when hardware is involved.
We start by understanding the physical environment your devices will operate in, the business decisions your data needs to support, connectivity constraints, power requirements, expected device count, and data volume.
Based on the requirements, we design the full-stack architecture - device hardware selection, firmware approach, communication protocols, cloud platform, data pipeline design, storage strategy, and application layer.
For new use cases or technically uncertain elements, we build a focused PoC that validates device connectivity, data transmission reliability, and sensor accuracy before committing to full development.
Parallel development across firmware, cloud backend, data pipeline, and application layer - with weekly integration checkpoints. We use hardware-in-the-loop testing throughout, not just at the end.
We test in conditions that approximate the real deployment environment - temperature ranges, connectivity variability, concurrent device counts, data volume spikes, and edge cases like power loss mid-transmission.
Phased rollout starting with a pilot deployment, with monitoring and rollback capability built in. Post-deployment, we set up remote device management - OTA firmware updates, health monitoring, and alerting.
We're hardware-agnostic and cloud-agnostic. We recommend the right combination for your use case - not what we happen to have invested in.
ESP32, STM32, Nordic nRF52, Raspberry Pi, NVIDIA Jetson, and custom PCB coordination for production hardware.
C, C++, Rust, MicroPython, FreeRTOS, Zephyr RTOS, and embedded Linux (Yocto, Buildroot) for reliable device software.
MQTT, CoAP, AMQP, HTTP/S, BLE, Zigbee, LoRaWAN, NB-IoT, Modbus, OPC-UA, and BACnet across industrial and consumer IoT.
AWS IoT Core, Azure IoT Hub, Google Cloud IoT, ThingsBoard, Losant, and custom MQTT brokers (EMQX, HiveMQ).
AWS Greengrass, Azure IoT Edge, Balena, Docker on embedded Linux, TensorFlow Lite, ONNX Runtime, and OpenVINO.
InfluxDB, TimescaleDB, Apache Kafka, AWS Kinesis, Spark, Flink, Elasticsearch, Grafana, and Apache Superset.
IoT investment is justified by the operational and financial outcomes it produces. Here's what businesses typically gain once a production-grade IoT system is running.
Sensor data from equipment reveals early signs of failure - vibration patterns, temperature drift, current draw anomalies - before the equipment breaks. Moving to predictive maintenance typically reduces unplanned downtime by 30–50%.
Managers and operations teams stop relying on manual reports and shift handovers. Live data from the factory floor, fleet, or facility is available to anyone who needs it, from any device, at any time.
Automated monitoring and control of energy consumption, water usage, HVAC, and lighting typically reduces utility costs by 15–35% in commercial and industrial environments - with the data to prove it.
Continuous sensor monitoring of environmental conditions, process parameters, and product quality replaces manual spot checks, reduces defects, and creates the audit trail regulated industries require.
For companies building connected products, IoT enables subscription revenue from data services, remote monitoring offerings, and differentiated features competitors without connectivity can't match.
Manual processes that require a person to be physically present don't scale. IoT automation removes the physical constraint and lets operations scale without proportional headcount growth.
Whether you have a well-defined IoT project, a rough concept, or an existing system you need to fix or scale - we're happy to have a direct conversation about what it would take to get there.
Typically responds within one business day
Everything you need to know.
A focused proof of concept with off-the-shelf hardware typically runs $15,000–$40,000 USD. A full production IoT system - custom firmware, cloud platform, data pipeline, and dashboard - is usually $80,000–$300,000+ depending on device count, protocol complexity, real-time requirements, and whether custom hardware is involved. We provide a detailed estimate after a discovery conversation.
A proof of concept is typically 4–8 weeks. A full production system - including field testing and phased deployment - usually takes 4–9 months. Hardware introduces lead times that pure software projects don't have, and field testing in real conditions always surfaces things that lab testing misses.
We handle hardware selection, vendor evaluation, and firmware development, and we coordinate closely with PCB design and hardware manufacturing partners when custom hardware is required. We don't run a manufacturing facility, but we manage the hardware development process as part of the overall project.
AWS IoT Core is the most mature and feature-complete managed IoT platform and is the right default for most projects. Azure IoT Hub is a strong choice if you're already deeply in the Microsoft ecosystem. GCP IoT is best when your primary need is large-scale data processing and ML. For some use cases, a self-hosted broker (EMQX, HiveMQ) on your own infrastructure is the right answer.
We treat IoT security as a first-class engineering requirement. This means device identity provisioning with X.509 certificates, mutual TLS for all device communication, firmware signing and verified boot, encrypted storage on device, secure OTA update mechanisms with rollback capability, network micro-segmentation, and regular vulnerability scanning.
We design for it from the start. Depending on the use case, this means local data buffering with store-and-forward when connectivity resumes, edge processing so critical decisions don't depend on the cloud, graceful degradation modes, and alerting when a device goes offline unexpectedly.
Yes. Enterprise IoT almost always involves integrating device data with existing operational and business systems. We have experience integrating with SAP, Oracle, major SCADA platforms, custom MES systems, and ERP systems via REST APIs, message queues, and direct database integration.
Yes - this is a common scenario. We assess what you have, identify the gaps causing problems (usually data pipeline reliability, security, scalability, or dashboard usability), and improve incrementally. We avoid recommending a full rebuild unless the existing architecture genuinely can't support what you need.
