Industry Solutions

How do you build AI agents for automotive?

Automotive agents triage warranty claims, answer dealer and technician questions from service documentation, cluster quality signals and summarise engineering records. The pattern that holds up uses function calling over read-only systems, retrieval for grounded answers, and a human approval gate for any claim, recall or design decision. Plugsky supplies the compatible API and models.

Key facts

Agent stackChat completions, function calling and embeddings are live
Use casesWarranty triage, dealer and technician Q&A, quality clustering, engineering doc search
Retrievalplugsky-embed plus RAG over service manuals and bulletins
ToolsRead-only tools for claims, parts and documentation systems
Models30+ models; small aliases for classification, stronger ones for synthesis
ApprovalHuman gate before claim approval, recall action or design change
Pricing modelFlat monthly self-serve plans with unlimited fair-use usage
RoadmapBatch and fine-tuning endpoints are coming soon

TL;DR

  • Triage warranty claims by symptom, parts and history before an adjuster reads them.
  • Answer dealer and technician questions from current service documentation.
  • Cluster repeat failures into quality signals for engineering review.
  • Keep approval for claims, recalls and design changes with humans.
  • Track first-time-fix guidance and misroute rate as success metrics.

How it works, step by step

  1. Choose one workflow such as warranty triage or technician Q&A.
  2. Expose read-only tools for claims, parts availability and documentation lookup.
  3. Index service manuals, bulletins and historical claims with stable document ids.
  4. Require citations and a structured output for every recommendation.
  5. Route claim approval and any safety-related issue to a human reviewer.
  6. Log tool calls and model versions, then measure triage accuracy and review time.
1Choose one workflowsuch as warrantytriage or2Expose read-onlytools for claims,parts availability3Index servicemanuals, bulletinsand historical4Require citationsand a structuredoutput for every5Route claimapproval and anysafety-related6Log tool calls andmodel versions,then measure triage

Try it yourself

Open the function calling schema generator →

Automotive use cases for agents

The industry runs on structured claims and dense technical documentation:

  • Warranty triage: classify claims by symptom, component and coverage, and assemble the evidence an adjuster needs.
  • Dealer and technician support: answer repair and procedure questions from current manuals and bulletins with citations.
  • Quality clustering: group repeat failures by symptom and part for engineering review.
  • Engineering document search: find the right requirement or test record across large document sets.

The agent recommends; adjusters, engineers and dealers decide.

Architecture and data boundaries

Build the agent around read-only access and explicit evidence:

  • Retriever: service manuals, bulletins, prior claims and parts data.
  • Classifier: symptom, component and urgency labels in a strict schema.
  • Tools: claim lookup, parts availability and documentation search, all read-only.
  • Composer: a recommendation with citations and a confidence note.
  • Reviewer: a human approves the claim or escalates the safety issue.

Connected-vehicle data, customer identity and dealer commercial data should be handled under your own privacy rules, with references preferred over raw records.

Safety, recall and quality guardrails

Any issue that could indicate a safety defect must bypass the agent and reach a qualified engineer immediately. Define that rule before launch and test it with adversarial examples. Keep claim approval and recall decisions with accountable people, and preserve an audit log of the prompt, sources, model version and reviewer for each case. Where a market requires local processing or a partner restricts data location, run the workload in a VPC, on-prem or air-gapped deployment; the compatible API means the agent code stays the same.

Pilot and metrics

Prototype on plugsky-micro or plugsky-lite with one region's claims and documentation, then use the 14-day full-access trial to test a stronger model on complex cases and clustering. Measure triage accuracy, first-time-fix guidance quality, adjuster minutes per claim and safety escalations caught. Self-serve plans are flat monthly with fair-use usage, so claim volume spikes are predictable. Keep prompts versioned and re-run the evaluation set whenever manuals or bulletins change.

Honest comparison

CapabilityPlugskyTypical agent frameworkBuilding in-house
Tool callingLive function calling over read-only claims and parts APIsFramework-specific adaptersYou build and maintain
Retrievalplugsky-embed plus RAG over manuals and bulletinsAdd-on or external storeYou integrate everything
Approval gateYou enforce claim and safety escalation in codeVariesYou design controls
DeploymentCloud, VPC, on-prem and air-gappedUsually cloud-onlyYou operate GPUs and serving
PricingFlat monthly self-serve, fair-use usageVaries by framework and modelGPU plus operations cost

Frequently asked questions

Can the agent approve warranty claims?

No. It should assemble evidence and recommend, while an adjuster approves the claim and any safety-related issue goes to a qualified engineer.

Which endpoints do automotive agents use?

Chat completions, function calling, JSON mode and embeddings are live. Batch and fine-tuning endpoints are coming soon.

How do we keep technician answers accurate?

Index current manuals and bulletins, require citations to the specific section, and re-index whenever documentation is updated.

Is there a free plan?

Yes. The free plan includes plugsky-micro and plugsky-lite with no credit card, enough to prototype claim triage and documentation Q&A.

How does pricing work?

Self-serve plans are flat monthly with unlimited fair-use usage. See the live pricing page for current plans and enterprise options.

Can we use connected-vehicle data?

Handle it under your own privacy rules and prefer references over raw records; keep personal and location data out of prompts unless a documented basis exists.

How should we measure it?

Track triage accuracy, adjuster minutes per claim, first-time-fix guidance quality and safety escalations caught rather than raw throughput.