IoT and Edge AI Engineering

Cloud AI sends data to a remote server for inference and receives results back — this introduces latency (typically 50–500ms round-trip) and requires reliable network connectivity. Edge AI runs inference on a device physically close to or on the machine generating the data — latency drops to 1–10ms, and the system works even when network connectivity is degraded. For manufacturing, the distinction matters for: quality control (vision-based defect detection needs sub-10ms to keep up with production line speeds); safety monitoring (operator proximity detection can't wait for a cloud round-trip); predictive maintenance (running anomaly detection on sensor streams locally rather than batch-uploading to cloud). The hardware for edge inference ranges from NVIDIA Jetson modules for vision workloads to microcontrollers running TensorFlow Lite for simpler anomaly detection on sensor data. We select the right inference hardware for the latency and power constraints of your specific use case.

A digital twin is a software model of a physical asset (a machine, a production line, a facility) that is updated in real time from sensor data, enabling you to simulate, predict, and optimise the physical system without interrupting it. The business cases where a digital twin has clear ROI: predictive maintenance (the twin predicts failure before it occurs by modelling wear patterns across the sensor data history); capacity optimisation (simulating production schedule changes against the twin before committing to them physically); remote monitoring (inspecting the state of equipment across multiple sites without physical presence); and quality control (correlating production conditions at the moment of a defect with the twin's state to identify the root cause faster). Digital twins are not universally worth building — they require good sensor coverage, reliable data pipelines, and ongoing calibration. We assess fit during the discovery sprint rather than recommending them as a default.

The integration between IoT sensor data and ERP (SAP, Dynamics 365, Odoo) or MES (Manufacturing Execution Systems) is the layer where most industrial IoT projects stall. IoT generates high-frequency time-series data (thousands of events per second per machine); ERPs and MES expect structured, lower-frequency transactional records. Bridging them requires: an IoT data platform (AWS IoT Core, Azure IoT Hub, or an on-premises MQTT broker) to receive and buffer raw sensor streams; a transformation layer that aggregates sensor events into business-meaningful records (e.g., shift production summaries, quality hold events, maintenance alerts); and an integration service that writes the transformed records into the ERP or MES in the format those systems expect. OPC-UA is the standard protocol for connecting to industrial control systems (PLCs, SCADA) that most manufacturing ERPs also support. We architect and build this integration stack as a defined deliverable — not an afterthought added after the IoT devices are already deployed.

Protocol selection depends on the industrial environment and device constraints. MQTT is the standard for internet-connected IoT devices: lightweight, reliable over unreliable networks, widely supported by cloud IoT platforms (AWS IoT Core, Azure IoT Hub, Google Cloud IoT). OPC-UA is the standard for industrial automation: used by PLCs, SCADA systems, and modern CNC machines — it provides standardised read/write access to machine data. For legacy equipment that only exposes Modbus or other older protocols, we deploy protocol translation gateways (often running on edge devices) that convert legacy protocols to MQTT or OPC-UA for the rest of the architecture. For time-series data storage: Apache Kafka or AWS Kinesis for high-throughput streaming; InfluxDB or TimescaleDB for time-series querying; object storage (S3, Azure Blob) for raw sensor archive. We select the protocol and storage stack based on your device ecosystem and data volume, not based on a preferred technology.

Pricing follows the engagement model. Discovery sprints are fixed-price for a fixed timebox. Build engagements are scoped after discovery — fixed-scope, time-and-materials, or outcome-linked. Managed services are billed monthly with defined SLAs. Total cost depends on scope, integration complexity, QA depth, and post-launch support — we publish a transparent breakdown for buyers evaluating their options.

Every engagement starts with a fixed-scope, fixed-price discovery sprint — typically 2 weeks. You leave with a written roadmap, risk register, cost estimate, and timeline before committing to a build. We then offer four engagement models: fixed-scope builds, time-and-materials, outcome-linked retainers, and managed services post-launch. If discovery doesn't justify proceeding, you can stop there — no obligation.