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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
期間: A modelled 12-week engagement
2026年7月

運用コンテキスト

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.

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

課題

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

ソリューション

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.

実装フェーズ

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

テクノロジー&アプローチ

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

想定する成果

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

代表的なエンゲージメントにおけるサービス

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

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他の実績を見る
Connected-Vehicle AI: From Pilot Fleet to Production Rollout — Engagement Playbook