Generative AI: Automotive

Generative AI for automotive

Automotive runs on dense technical documents: repair manuals, service bulletins, requirements specifications, warranty claims and supplier quality reports. We build generative AI that reads them, connects them to diagnostic and dealer data, and gives technicians, engineers and dealer staff a cited answer in seconds, while keeping AI out of safety critical vehicle functions unless the safety case is done properly. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

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Key highlights

We build service copilots, warranty claim AI, requirements assistants, dealer agents and supplier document processing for the automotive value chain.
Technician answers are grounded in your repair manuals, service bulletins and DTC history, with the source page shown every time.
Warranty and quality decisions stay with people: AI prepares the case, flags anomalies and drafts the response.
Engineering data can stay in your own cloud with self hosted open weight models, which suits TISAX and supplier confidentiality.
A first use case, such as a repair manual copilot for one model line, usually reaches a guarded pilot in 8 to 12 weeks.

Why AI projects stall in automotive

Automotive companies have strong engineering discipline, and that is exactly why loose chatbot pilots fail their reviews. AI has to meet the same standard of traceability as everything else.

Technicians lost in documentation. Modern vehicles need thousands of pages of repair information and a steady flow of service bulletins. Technicians spend diagnostic time searching, and comebacks rise when the wrong procedure is used.

Warranty costs and claim leakage. Dealer claims arrive with free text descriptions, photos and labour codes. Reviewers cannot read every one, so invalid claims get paid and real quality signals are spotted late.

Requirements nobody can trace. Specifications run to thousands of requirements across OEM and supplier documents. Checking consistency, finding duplicates and tracing them to tests is slow and manual work in DOORS or Polarion.

Supplier confidentiality and TISAX. OEM data shared with suppliers is under strict information security terms. Sending drawings or specifications to a public AI service is not an option, and projects stop at the security review.

Dealer leads that go cold. Web, phone and marketplace enquiries arrive out of hours and across many systems. Slow first responses lose sales, and service bookings are lost to independent garages.

Safety questions around in car AI. Voice assistants and driver facing features raise functional safety and distraction concerns that a cloud chatbot prototype was never designed to answer.

Use cases

Generative AI use cases we build for automotive

Each use case is grounded in your documents and data, measured against a test set, and keeps a qualified person responsible for the decision.

Service technician copilot

RAG over repair manuals, wiring diagrams, service bulletins and past repair orders, combined with the vehicle's DTCs and VIN data. The technician gets a cited diagnostic path and procedure, and confirmed fixes improve future answers.

Warranty claim review

Document extraction and classification read claim narratives, labour codes and photos, check them against coverage rules and repair history, flag likely invalid or duplicate claims and cluster emerging failure patterns for quality engineers.

Requirements and specification assistant

Reads requirement sets from DOORS, Polarion or Word, finds ambiguous, duplicate and conflicting requirements, suggests test cases and traces, and answers engineering questions across specifications with source references.

Dealer sales and service agent

A tool using agent on web chat, SMS and phone that answers vehicle and stock questions from your inventory feed, books test drives and service appointments in the DMS, and passes qualified leads to sales staff with a summary.

Supplier quality and PPAP documents

Extracts data from PPAP submissions, 8D reports, certificates and supplier quality documents into structured records, checks completeness and drafts responses for supplier quality engineers to review.

In vehicle voice assistant prototypes

Prototypes of LLM voice assistants for owner manual questions and comfort functions, with on device or hybrid models, strict limits on which vehicle functions can be called, and evaluation for latency and distraction.

How we deliver

From one use case to a production AI capability

01
Use case discovery and data readiness
We rank use cases with aftersales, engineering, quality or dealer teams, then check the data: manual formats, DTC and repair order history, claim data, requirements tools and the information security classification of each source.
02
Evaluation harness and prototype
Technicians, reviewers or engineers label a golden set of real cases. The prototype is scored on accuracy, citation correctness and safe refusal on every change, against the same set.
03
Guarded pilot with experts in the loop
One workshop, one claim queue or one engineering team uses the tool on real work. Every output is reviewed, and corrections feed the test set and the knowledge base.
04
Production rollout with monitoring
Rollout across dealers, plants or programmes with SSO, role based access, usage and cost dashboards, drift alerts and a full log of prompts, sources and outputs.

AI governance for automotive

Automotive AI has to fit an industry built on safety cases, cybersecurity management and supplier confidentiality. We design for that from the start.

TISAX aligned handling ISO/SAE 21434 awareness UNECE R155 and R156 ISO 26262 and ISO/PAS 8800 boundaries EU AI Act GDPR and UK GDPR FTC Safeguards Rule (US dealers) Data residency by region Self hosted model option

Assistance tools stay out of safety functions. Service, warranty, engineering and dealer copilots assist people and never control a vehicle. Where AI is proposed inside the vehicle, we scope it against ISO 26262 and the newer ISO/PAS 8800 guidance on AI in road vehicles, and limit what an LLM can call. Software updates and connected services follow your UNECE R155 and R156 processes.

Engineering and supplier data stays protected. Drawings, specifications and supplier data can be processed by open weight models self hosted in your own cloud region, with access mirroring your PLM and requirements tool permissions. That keeps AI inside the information security boundary your TISAX assessment covers.

Customer and vehicle data is handled carefully. VIN, location and owner data is redacted before external model calls where needed. US dealer projects follow the FTC Safeguards Rule for customer financial information, and customer facing agents disclose that they are AI, as EU AI Act transparency rules require from August 2026.

Models and tooling we use for automotive AI

Model choice is made per use case after testing on your documents. Integrations connect AI to the engineering, aftersales and dealer systems you already run.

Anthropic Claude OpenAI GPT models Azure OpenAI and AWS Bedrock Llama and Mistral self hosted LangGraph agents pgvector and Elasticsearch hybrid search Azure Document Intelligence and Textract IBM DOORS and Polarion integration CDK and Reynolds DMS integration Twilio and SIP voice agents ONNX Runtime and on device models Langfuse tracing and evals

FAQ

Common questions

Can generative AI be used in safety critical vehicle functions?

Not as a shortcut. Our copilots for service, warranty, engineering and dealers assist people and never control a vehicle. In vehicle AI needs a proper safety case under ISO 26262 and ISO/PAS 8800, so we keep LLM features to non safety functions and restrict which vehicle functions they can reach.

Is our engineering and supplier data safe?

Yes, with the right deployment. Sensitive engineering data can stay in your own cloud with self hosted open weight models, access mirrors your PLM and requirements permissions, and enterprise APIs are used only with no training on your data and in your chosen region.

How do you prevent wrong repair or diagnostic answers?

By grounding every answer in your repair manuals, bulletins and repair history, showing the source page, and refusing when the source does not cover the vehicle or fault. Accuracy is tested on a labelled set of real repair cases on every release, and the technician always has the original procedure.

How long does an automotive AI project take?

Usually 8 to 12 weeks to a guarded pilot for one use case. That covers discovery, a labelled test set, the prototype and a pilot with one workshop, claim queue or engineering team. Wider rollout follows in phases.

Should we buy an automotive AI product or build our own?

Buy when a DMS, PLM or warranty vendor feature covers your workflow and data terms. Build when the value is in combining your own manuals, claims, requirements and dealer data, or when you are shipping AI to your own customers. We often extend bought platforms with custom agents.

How is an automotive AI project priced?

Usually as a fixed price discovery and prototype phase, then a pilot and rollout scoped from measured results. Running costs depend on usage volume and whether models are hosted or self hosted, and we estimate them before you commit. See our pricing page for engagement models.

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