Lifecycle stage — Build
Every Physical AI system is only as good as what it perceives. Perception and sensor-fusion engineering is the discipline of combining heterogeneous sensors — vision, depth, LiDAR, radar, inertial, force/torque — into a single, time-synchronised world-model that downstream reasoning can trust, running at the edge with sub-10ms latency and degrading gracefully when a sensor is occluded, miscalibrated, or fails. This is the Sense layer of the Physical AI Stack delivered as a capability: from sensor selection and calibration through fusion architecture to an edge-deployed perception model your operations team can validate. It is the engineering behind the live vision demos on this site — plant-audit and defect detection run on the sovereign Pixtral/Mistral stack — generalised to your machines and your sensors.
Sensors disagree. A camera, a LiDAR, and a radar see the same scene differently, at different rates, with different failure modes — fusing them into one consistent estimate (and knowing which to trust when they conflict) is where most perception projects stall.
Calibration and time-sync are unglamorous and decisive. Extrinsic calibration drift and a few milliseconds of clock skew between sensors silently corrupt the fused world-model long before anyone sees a wrong decision downstream.
Production conditions are not the lab. Glare, dust, vibration, rain, low light, and partial occlusion break perception models that demoed perfectly on clean data — and a safety-relevant miss in the real world is not a rounding error.
A capability engagement scoped to your machines and your sensors, delivered sovereign-first on infrastructure you own.
Audit the existing sensor suite, define the perception requirement (range, resolution, latency, safety relevance), and establish a repeatable intrinsic/extrinsic calibration and time-synchronisation procedure.
Design the fusion stack — early/late/deep fusion as the task demands — with explicit handling of sensor disagreement, dropout, and confidence, so the world-model carries uncertainty rather than hiding it.
Train and optimise vision/multi-modal models to run on edge accelerators within the latency and power budget, on the sovereign Mistral/open-weight stack where language or reasoning is involved.
Build the test set that includes the failure cases — occlusion, glare, miscalibration, sensor loss — and validate detection and fusion behaviour against it before anything reaches an actuator.
Robotics integrators, automotive and ADAS teams, industrial and energy operators, and AMR/AGV builders that need a perception stack robust enough for production — not a demo that only works on clean data. Especially relevant where perception feeds a safety-relevant decision and the sensor suite is heterogeneous.
No — this is an engineering capability, not a hardware resale. I work with the sensor suite you have or help you specify one vendor-neutrally, then engineer the perception and fusion stack on top of it.
Yes. Edge and on-prem deployment is the default — sub-10ms decisions should not depend on a cloud round-trip, and your operational data should not have to leave your infrastructure.
Perception produces the world-model; the safety-case service engineers the evidence that the resulting system is safe. For safety-relevant perception they are usually scoped together.
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30 minutes. I diagnose your situation, tell you honestly whether this service fits — and if it doesn't, what does.