Automotive & Mobility

From plant floor toconnected vehicle

Telemetry from the fleet, quality data from the line, warranty claims from the dealer network, parts data from tier-one suppliers. Each in its own system, each with its own idea of what a vehicle is.

10B+
Records processed daily
Across production pipelines
VIN
Level traceability
Build to warranty to recall
99.9%
Pipeline uptime SLA
Telemetry cannot pause
35%
Average cloud cost reduction
At telemetry volumes
The architecture

One objective in.Your whole estate, moving.

The governed layer sits at the centre of your operations. It reads every signal across Vehicle telemetry, Production data, Quality checks and the rest of your estate, and closes every loop back to your business and regulatory goals.

Vehicletelemetry
Productiondata
Qualitychecks
Dealersystems
Warrantyclaims
Supplierfeeds
Faultcodes
Live

Automotive Governed Data Layer

IntelliBooks · Platform agnostic · Your cloud · On-prem capable

Streaming ingestion
High-volume telemetry through Kafka with backpressure handled
VIN resolution
One vehicle identity linking build, sale, service and warranty
Governed models
Vehicle, component, dealer and claim conformed to an agreed grain
Agent registry
Warranty triage, quality anomaly and supplier scorecard agents
Orchestration
Batch and streaming side by side with deterministic fallbacks
Lineage & audit
Component genealogy traceable for recall and regulatory response
Databricks · Snowflake · BigQuery · Redshift · Iceberg — alongside your MES, DMS and ERP
Any warehouse · Any cloud · Any orchestrator · Bring or build agents
Warrantyinsight
Qualityalerts
Recalltracing
Dealerperformance
Supplierscoring
Maintenancealerts
Fleetreporting
What does your organisation actually need?

The right question changes the answer.

Most automotive teams have modernised in pockets. We start with why those pockets never joined up, and what it takes to run the whole estate on one governed layer.

Components pass through suppliers, plants and dealers before they reach a customer. How long would it take to identify every vehicle carrying one batch?


Component genealogy modelled to vehicle level makes the affected population a query against build and supplier lot data, turning a multi-day reconstruction into an afternoon.

Warranty claims, build records and diagnostic codes usually live in separate systems. Can you separate genuine component failure from inconsistent claim coding?


Joining claims to build data, fault codes and dealer patterns separates real failure from coding variance, so remediation targets the actual cause rather than the loudest symptom.

Connected vehicles generate enormous volumes of telemetry, most of which is stored rather than used. Which decisions does yours currently inform?


Modelled against the vehicle and component hierarchy, telemetry connects to warranty, quality and engineering. Without that model it remains storage you pay for and rarely query.

Quality signals appear on the production line before they appear in warranty cost. Are you seeing them early, or reading them later?


Line-side measurement joined to field telemetry and fault codes surfaces drift weeks before it becomes a claim pattern, which is where the recoverable cost sits.

From objective to outcome

This is how the work actually runs.

01

Your plant and dealer systems stay.

Your ERP, your manufacturing execution system, your dealer management platform, your process historian. Connect what you have. Nothing migrates off its system of record.

Zero rip-and-replace
02

Scope from one question worth answering.

Not a data lake programme. A question. "Which components drive warranty cost?" "Which dealers code claims inconsistently?" "Which build weeks carry elevated risk?" We work back from that.

Question-first scoping
03

The right agents activate, inside limits you set.

Warranty claim triage, quality anomaly detection, component genealogy tracing, supplier scoring, telemetry enrichment. Autonomy per process: assistive for engineering judgment, delegated for claim triage, autonomous for anomaly detection.

Staged autonomy, earned not assumed
04

Failures surface early. Recalls scope in hours.

Component genealogy and build records carry lineage, so affected populations are a query and homologation evidence is produced on demand rather than assembled under deadline.

Traceable on demand
Coverage

We map to how you already run.

Tell us which of these hurts most and we start there — not with a platform rollout.

Connected vehicle telemetry

High-volume signal ingestion modelled against the vehicle hierarchy.

Warranty analytics

Claims joined to build, component and dealer data to isolate true cause.

Early quality warning

Line-side SPC combined with field signals to surface drift pre-claim.

Recall traceability

Component genealogy to VIN level so affected populations are a query.

Dealer performance

Sales, service and claim behaviour compared consistently across the network.

Supplier scorecards

Quality, delivery and cost by supplier and by part, from primary data.

Parts & aftermarket

Demand forecasting and inventory positioning across the service network.

Manufacturing OEE

Availability, performance and quality computed from MES and historian data.

Predictive maintenance

Fleet and plant equipment failure prediction from sensor history.

Supply chain visibility

Tier-one and tier-two flows tracked against production schedules.

Homologation evidence

Regulatory test and compliance data assembled with provenance.

Fleet & mobility

Utilisation, charging and duty-cycle analytics for fleet operators.

The question you are already asking

Your data. Your platform. Your call.

It runs in your cloud

Deployed in your own account and region, which matters when telemetry crosses jurisdictions with differing data-protection regimes.

Governed at every step

Every agent action logged with its query and identity. Personal data in telemetry classified and masked according to the jurisdiction it was collected in.

You keep what we build

Models, pipelines and lineage are yours. Vehicle and component data models are core IP and should not sit inside a vendor runtime.

Built for telemetry economics

At these volumes, architecture decisions are cost decisions. Partitioning, tiering and retention are designed against your actual query patterns.

IATF 16949Data support
ISO 27001Aligned
SOC 2Aligned
GDPRTelemetry-aware
UNECE WP.29Evidence support
On-premisesDeploy option
Works with your stack
DatabricksSnowflakeGoogle BigQueryAWS RedshiftApache IcebergApache KafkaApache SparkdbtApache AirflowClickHouseERP integrationTrino

If you are thinking it, it is answered here.

Yes. Pipelines in production process 10B+ records daily. At that scale the architecture decisions — partitioning, tiering, retention — are what determine both query performance and cost, so they are designed against your real query patterns rather than a default.
No. Those remain the systems of record. We build the governed layer where their data reconciles with telemetry, warranty and supplier information.
Signals that constitute personal data under GDPR are classified during discovery and masked according to the jurisdiction of collection. Access is evaluated at query time rather than baked into the stored data.
That is a primary use case. Component genealogy modelled to VIN level means the affected population is a query against build and supplier lot data, not a multi-day reconciliation exercise.
Expected. Supplier feeds arrive as EDI, flat files, spreadsheets and APIs with differing cadences. Ingestion normalises them and flags drift when a supplier changes format without telling anyone.
A two-to-four week assessment covering source inventory, telemetry volume profiling, data quality and a costed roadmap against one question worth answering. The output is yours regardless.

Set the objective. We will scope it honestly.

Bring us the estate as it actually is, deadlines included. Two to four weeks later you have a costed roadmap you own — whether or not you continue with us.