Device provisioning, telemetry pipelines, APIs and OTA infrastructure — architected so scaling the fleet doesn't mean rewriting the backend.
The backbone that lets a pilot of ten devices become a fleet of a hundred thousand without a rewrite.
Pulled from our case-study library — same AI/human split shown in full, industry context included.
A wearable medical patch continuously captures patient vital signs and securely transmits physiological data for remote monitoring.
A connected platform collects operational telemetry from distributed solar micro-inverters, giving fleet-wide visibility into performance, health and faults.
A central smart-home router/gateway connects distributed home sensors on one platform for device provisioning, monitoring, automation and user control.
AWS IoT and Azure IoT most often, chosen by what your existing stack or team already uses — we don't default to one vendor regardless of fit.
Role-based access and tenant isolation are designed into the schema from day one — retrofitting multi-tenancy after launch is one of the more expensive mistakes we help teams avoid.
AI drafts API scaffolding, infra-as-code and schema proposals; a senior engineer owns security architecture and every cost/scale tradeoff.
Yes — we audit the current architecture, telemetry pipeline and security posture before proposing whether to harden, re-platform, or leave specific pieces alone.
Bare-metal to RTOS, matched to your power and timing budget.
Boards designed for manufacturability, not just the bench.
Fleet-scale telemetry and APIs behind the screen.
Every service above this one uses this layer somewhere.
