AI engineering for manufacturing and automotive

Manufacturing inspection and automotive perception share an evidence shape: a model that scores well in lab evaluation can still fail on the production line or the open road because the slice that breaks it was never in the training set, the eval harness never saw the new defect class, and the regression gate was missing when the model was swapped. The fix is engineering evidence around the model, not the model itself. We work on civilian transport and production-manufacturing inspection only.

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Where the Engineering Bottleneck Lives

On the line, an industrial CV inspection deployment hits a defect class the original eval set never covered, a slice-level regression production monitoring is not catching, or a release-gate question after a model swap.

In the vehicle, a civilian perception programme — ADAS, fleet perception, test infrastructure — needs engineering validation evidence on a perception model or sub-component that the safety and validation roles you already have can read and challenge. Not certification, not safety-case authorship — the engineering evidence those roles use to do that work.

Industrial CV Inspection

Quality-control deployments fail in a specific way: headline accuracy on the original eval set is fine, but a new product variant, defect mode, lighting condition, or model swap pulls slice-level performance off-target without surfacing in monitoring. We build the eval harness with the slice cuts the operations team cares about, regression suites against historical defect cases, gating that holds release until those cuts are within tolerance, and a runbook the line team can rerun.

Lands in the Production AI Monitoring Harness — 4–10 weeks, milestone or fixed-price.

Automotive Perception (Civilian Vehicles)

Civilian-vehicle perception — ADAS, driver assistance, fleet perception, test infrastructure — sits at the engineering-evidence end of the safety case, not the sign-off end. We build eval harnesses on the slices your safety team flags, regression suites against historical edge cases, slice-level monitoring that reflects the open-road distribution, and structured comparison of perception sub-components. We do not author safety cases or certify against functional-safety standards; the evidence is the input the safety roles you already have use.

Lands in the Production AI Monitoring Harness — 4–10 weeks, milestone or fixed-price.

How We Work With Manufacturing & Automotive Teams

The Production AI Monitoring Harness has a fixed scope and a price tied to the outcome, and ends in something your team keeps and can re-run — the eval harness, the re-run script, the slice-level monitoring dashboard. We work alongside your safety, validation, and quality functions; we do not substitute for them, and we do not author safety cases or certify perception systems.

Heading into a line-release gate, a perception-validation review, or a production rollout? The Production AI Monitoring Harness page is the entry point — or contact us and we will route you.

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