IoT applications built by a team that has shipped them at fleet scale.
Most IoT projects do not fail at the device. They fail at the layer between the device and the decision — the telemetry pipeline that drops messages under load, the dashboard nobody can query, the update mechanism that needs a van visit. We build that layer, and we have run it in production across vehicle, asset and industrial deployments since 2011.
Device-side work bounded by the real constraints — power budget, thermal envelope, intermittent connectivity — rather than by what works on a desk.
Ingestion designed for the message rate you actually have, with backpressure and replay, so a burst does not silently lose data.
Processing at the device where bandwidth cost or latency demands it, sized against the power and storage the hardware really has.
Operational views your team can query, not fixed reports. Position, status and exceptions, at the refresh rate the job needs.
Remote firmware and configuration updates with staged rollout and rollback, because a bad update across a deployed fleet is expensive to undo.
Native iOS and Android apps for the people using the devices — diagnostics, alerts and control, built for patchy connectivity.
We work across the full stack where the project needs it, including where hardware constraints drive the software design. On vehicle intelligence work we have built against owned hardware, and the binding constraint is usually power, heat and storage rather than model or code quality.
Yes, and that is the more common starting point. The first question is what your devices already record, at what interval, and how long it is kept — that determines which questions can be answered at all, and it is worth settling before any new build.
A scoped pilot against existing devices is typically weeks rather than months. A full platform with firmware, pipeline, dashboards and companion apps is a longer engagement, and we would rather stage it so something is in the field early than deliver everything at the end.