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Automotive & Mobility

Connected-Vehicle AI: From Pilot Fleet to Production Rollout

How a connected-vehicle AI feature can move from a 200-car pilot fleet to a governed, multi-market production rollout

Representative scenario
Durée: A modelled 12-week engagement
juillet 2026

Contexte opérationnel

A European OEM or tier-1 context with a connected-vehicle platform, pilot-scale telemetry and an unresolved path through OTA delivery, fleet operations and market-specific constraints.

Taille: Representative: a European OEM or tier-1 with a connected-vehicle platform

Le Défi

A driver-behaviour AI feature performs well on a pilot fleet, but the path to a multi-market production rollout — OTA delivery, type-approval constraints, back-end scale — is undefined.

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An indicative starting point: a feature validated on ~200 vehicles that must scale to hundreds of thousands, across markets with different connectivity and regulatory profiles

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Model updates delivered ad hoc to the pilot fleet; production requires signed, staged OTA campaigns with rollback

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Cloud inference acceptable in the pilot; production latency and data-sovereignty budgets would push inference into the vehicle or to the edge

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UNECE R155/R156 software-update and cybersecurity management obligations not yet mapped to the ML lifecycle

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Telemetry pipeline sized for a pilot fleet, not for fleet-wide event volumes with lawful consent handling under GDPR

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No explicit acceptance criteria for what 'ready for rollout' means — the decision would currently be taken on demo impressions

Notre Solution

A representative engagement would deliver a production architecture for the feature — an in-vehicle inference budget, signed OTA model delivery, fleet observability — and a rollout plan gated by explicit acceptance criteria.

Readiness review first, then production architecture, staged hardening, and transfer to the OEM's team — each phase gated, nothing obliges continuation.

Phases d'Implémentation

1

Production readiness review

The pilot assessed across architecture, data, integration, reliability and governance — producing the prioritised blocker map and the rollout decision basis.

Weeks 1–2
2

Production architecture design

In-vehicle/edge inference split against the latency and sovereignty budget; signed OTA model-delivery pipeline aligned to R155/R156 processes; telemetry consent architecture.

Weeks 3–6
3

Hardening & staged rollout

Shadow-mode validation on the pilot fleet, market-by-market rollout gates, automated rollback criteria written down before the first campaign.

Weeks 7–10
4

Operate & transfer

Fleet dashboards, drift monitoring and campaign runbooks handed to the OEM's engineering organisation.

Weeks 11–12

Technologies & Approches

In-vehicle edge inferenceSigned OTA deliveryFleet telemetry & observabilityShadow-mode validationUNECE R155/R156 process alignmentGDPR consent architectureModel registry & versioningAutomated rollbackDrift monitoringStaged rollout gatesEvent-volume load testingCI/CD for embedded targets

Résultat visé

The intended outcome is a staged rollout behind explicit proceed, hold and rollback gates for each market, with the OEM team operating the system.

Services d'une Mission Représentative

Product Decision Review
Product Leadership Mission
Edge AI Architecture
OTA & Fleet Operations Design
Training & Capability Transfer

Vous reconnaissez cette situation ?

Discutons de comment nous pouvons vous aider à relever vos défis spécifiques et générer des résultats commerciaux mesurables.

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Connected-Vehicle AI: From Pilot Fleet to Production Rollout — Engagement Playbook