Automotive & Mobility AI Engineering

No, and we design so it does not. Safety-critical functions stay in certified, deterministic systems under ISO 26262. AI surfaces quality and failure signals for people and systems to act on, but it never takes a safety action on its own. That boundary is deliberate, because the cost of a wrong safety decision is not recoverable.

It finds the early signature of a defect in field and warranty data before it becomes a widespread, expensive problem or a recall. The value is in the lead time: catching a pattern across the first failures lets you act while it is cheap. We measure that on your data rather than promising a generic saving.

Yes, at the edge, because line decisions cannot wait for a cloud round trip. We quantize models to run on hardware next to the equipment, which also keeps operational data on-site. Cloud handles training and fleet-wide analysis, not the real-time pass-or-fail call on a part.

Telematics is personal data, so we minimize what is collected, keep it in your environment where required, and apply driver-privacy controls under GDPR. Connected-vehicle value and privacy are not in conflict when the system is designed for both, and regulators increasingly expect that it is.

You do. Your line, warranty, and telematics data, the trained models, and the integration code remain yours and can run within your own network. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single predictive maintenance build is a modest, weeks-long project, while a wider rollout across plant, warranty, and telematics systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from fewer defects, less downtime, and lower warranty and recall cost, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as predictive maintenance usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are predictive maintenance and warranty analytics, visual quality inspection, demand forecasting, and connected-vehicle anomaly detection. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.