Blueprint · Industrial IoT

Fleet monitoring and predictive maintenance platform

Telemetry from thousands of machines, ingested reliably, modelled as digital twins and turned into alerts a service team acts on.

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Layers

OperationsOTA, monitoring, SLA support
AppsOperator console and customer portal
DataTwins, time-series, anomaly models
CloudMQTT ingestion, identity, storage
EdgeGateway agent, buffering, local rules
DeviceSensors and controller firmware

Built for

Equipment manufacturers and operators who want to know a machine is failing before the customer does.

Typical timeline

Pilot fleet in 10 to 14 weeks

Typical team

4 engineers: embedded, cloud, data, front-end

Practices

What it includes

The components, and what each one is for.

01
Edge agent
Runs on the gateway or controller, buffers when the network drops, ships signed OTA updates.
02
Ingestion and storage
MQTT broker cluster feeding a time-series store, sized for bursts and cheap to keep.
03
Asset model
A digital twin per machine: configuration, state, history and health in one queryable object.
04
Anomaly detection
Models trained on your own vibration, temperature and cycle data, tuned to a false-positive budget.
05
Operator console
Fleet map, machine detail, alert triage and service history, built for technicians.
06
Fleet operations
Provisioning, certificate rotation, cohort-based rollouts and fleet-wide dashboards.

Stack we reach for

Rust or C
Zephyr
MQTT
EMQX
TimescaleDB
Kubernetes
Python
Grafana
Next.js

How it would run

Discovery first, then a pilot you can measure.

Every blueprint starts as a two-week discovery that adapts it to your hardware, your data and your constraints. The pilot that follows is scoped to prove the riskiest assumption first.

Read about our process

Shipped in this area