AI MVP Development for Automotive & Mobility

Automotive and mobility software fails in ways a generic AI build never anticipates. Signal data arrives as raw CAN bus frames and OBD-II PIDs, not clean JSON; a diagnostic trouble code (DTC) means nothing until it is joined to the VIN's build sheet, the ECU firmware version, and the repair-order history. We build AI MVPs for OEMs, tier-1 suppliers, fleets, dealer groups, EV-charging networks and mobility startups that treat the vehicle as the messy, safety-critical, intermittently-connected data source it actually is — and we ship the first production version in 2-3 weeks with 100% code ownership handed to your team.

Predictive maintenance is the use-case we get asked for most, and the naive version — a threshold alert on a single sensor — is worse than useless because it drowns fleet managers in false positives. We build remaining-useful-life (RUL) models that fuse time-series telematics (engine temperature, battery state-of-health, brake-pad wear, DPF regeneration cycles) with the vehicle's service history and duty cycle, so an alert says 'this specific injector on this VIN is about 1,800 miles from failure' rather than 'something is warm.' The ingestion layer speaks MQTT and the CAN/OBD-II reality, decodes VINs against the NHTSA vPIC database, and normalizes across mixed fleets before any model ever sees the data.

For connected-vehicle and driver-facing apps we integrate the standards teams actually live with: Smartcar and High Mobility for cross-OEM vehicle APIs, Geotab and Samsara for existing telematics fleets, ISO 15118 'Plug & Charge' and OCPP for EV charging sessions, and the CCC Digital Key spec (UWB/NFC) for phone-as-key experiences. On the retail and parts side we connect to dealer management systems (CDK, Reynolds & Reynolds, Dealertrack) and catalog data in the ACES/PIES format so an AI parts-lookup or trade-in valuation reflects real inventory, not a demo dataset.

Anything that touches the vehicle's motion or safety envelope lives under functional-safety and cybersecurity regimes, and we design the MVP to respect them from day one rather than bolt them on later. That means understanding where a feature sits on the SAE J3016 autonomy scale, keeping ADAS/perception experiments on the ISO 26262 (ASIL) and ISO 21448 SOTIF side of the line, and — critically for anything shipping to production vehicles — the UNECE WP.29 R155/R156 mandate for a Cybersecurity Management System and secure over-the-air updates. We are not selling a full ASIL-D certification in three weeks; we are making sure your MVP's architecture, data handling and OTA path won't have to be thrown away when it graduates from prototype to type-approval.

Computer vision is where a lot of mobility value hides: dashcam-based ADAS and driver-monitoring, automated vehicle-damage inspection for insurance and remarketing, tire and tread analysis, and lot/yard management from fixed cameras. We build these as pragmatic MVPs — a fine-tuned detection model plus a human-in-the-loop review queue and an accuracy dashboard — so an insurer or dealer can trust the estimate before it flows into a claim or a listing. On the language side, RAG over OEM service manuals, TSBs and DTC libraries turns a technician's plain-English symptom into the right diagnostic procedure, and agentic workflows can draft the repair order or trigger a parts-procurement request automatically.

Connected-car data is regulated personal data — location, driving behavior and biometrics from driver monitoring all fall under GDPR and increasingly under US state privacy laws and insurer usage-based-insurance (UBI) rules — so consent, data minimization and retention are part of the schema, not an afterthought. Fleet products additionally inherit FMCSA ELD and IFTA obligations. Across every engagement the deliverable is the same: a real, deployed AI MVP your users can touch, built by our team of 15+ engineers, with the entire codebase, models and infrastructure owned by you. We've shipped 18+ AI MVPs on this model, and the mobility ones lean heavily on the telematics, routing and parts-procurement patterns proven in the case studies below.