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PRANINNOVATIONSProduction Specialists

Physical AI

Your devices already collect the data. We make them act on it.

Vibration, temperature, current, acoustics. Most plants are already recording all of it and using almost none of it, because the gap between a sensor reading and a maintenance decision is a piece of engineering nobody owned.

Scope a fleet deployment
Production architecture for the AI IoT Development practice

AI IoT Development

The situation

Before we start

01

Data logged, never used

Historians hold years of readings that no one acts on, because nothing converts a trend into a work order.

02

Maintenance on a calendar, not a condition

Fixed intervals replace parts that were fine and miss the ones that were not. Condition monitoring changes what triggers the visit.

03

A fleet you cannot update

Devices in the field with no safe way to push a new model become frozen the day they ship. Provisioning and over-the-air updates are designed in from the start.

What working means here

Signed first. Measured after.

The value of each one is set with you during scoping, from your data and your line. What does not change is that they are written down first and published against afterwards, pass or fail.

  1. 01

    Lead time before failure

    How far ahead of the event the system raised it, against your maintenance history

  2. 02

    False-alarm rate

    Tuned during the pilot so the first month does not train people to ignore it

  3. 03

    Fleet availability

    Devices reporting and deciding, including through uplink loss

How it is assembled

Stage by stage

Each one ends in something you keep.

  1. Step 01

    Signal review

    What is already being recorded, at what rate, and whether the failures you care about are visible in it.

  2. Step 02

    Instrument the gaps

    Sensor selection, placement and commissioning only where the existing data cannot answer the question.

  3. Step 03

    Model against real failures

    Trained on your maintenance history, so predictions are anchored to events that actually happened.

  4. Step 04

    Roll out in waves

    A pilot group of devices first, with the OTA path proven before the fleet follows.

  5. Step 05

    Operate

    Fleet health, drift monitoring and scheduled model updates under an agreed SLA.

Scope

What the work includes

  • Signal review: what your sensors already produce and what is missing
  • Sensor selection and placement where new instrumentation is needed
  • Feature engineering and model development against your failure history
  • On-device inference sized for the hardware and power budget
  • Provisioning, identity and secure over-the-air model updates
  • Fleet health: connectivity, device state, model version, drift
  • Integration into maintenance and work-order systems
  • Alerting tuned so the first month does not train people to ignore it

Deliverables

What you keep

  1. 01Signal and instrumentation review
  2. 02Trained models with validation against failure history
  3. 03Provisioned device fleet with OTA update path
  4. 04Fleet health dashboard and alerting
  5. 05Integration with your maintenance system
  6. 06Runbooks and rollout plan

Typical stack

Python · PyTorch · ONNX Runtime · MQTT · Modbus · Time-series databases · ARM / Jetson · OTA update pipelines

Construction · Vision

Zone and PPE monitoring across eleven active sites

Site cameras were recording for insurance but nobody was watching them live. We added edge units behind the existing camera estate to flag exclusion-zone entries and missing PPE, and routed alerts to the supervisor on shift.

Read the engagement
11
sites live on existing cameras
99.9%
per-site availability
< 4 s
detection to supervisor alert

Start

How AI IoT Development begins

Fleet deployment $90K–300K+ · Managed fleet from $4.5K/month

Where an engagement lands inside its band is set by data readiness, integration depth, the reliability bar, evaluation burden, deployment constraints and usage economics. All six, explained.

Thirty minutes on the problem and where it happens, then a fixed-price pilot if it looks like a fit.

Scope a fleet deployment

FAQ

Asked before signing

Often not. The signal review starts with what is already installed, and we only specify new instrumentation where the existing data genuinely cannot answer the question.

Enough labelled events to validate against. Where failures are rare we frame the problem as anomaly detection against normal running instead of prediction, and say so up front.

Signed artefacts over a staged rollout: a canary group, then waves, with automatic rollback on health regression. On restricted sites this runs through your controlled-media process instead.

Devices decide locally and buffer results. The uplink carries structured events and model updates, so intermittent connectivity delays reporting rather than stopping the system.

Often paired with

Where this fits with the rest

  1. 01Physical AI

    Edge AI & Computer Vision

    Detection, inspection, tracking and safety on-device. Milliseconds, and no network dependency.

  2. 02Operate

    Managed Edge & Model Fleet

    Drift, retraining, OTA rollout, device health and uptime reporting, under an agreed SLA.

  3. 03Physical AI

    Vision Systems Integration

    Cameras, optics, lighting, enclosures, and the PLC, SCADA and MES interfaces that make results act.