Key facts
| API surface | OpenAI-compatible /v1/chat/completions; keep your existing SDK |
| Grounding | Embeddings and RAG are live for engineering, service and quality knowledge search |
| Models | 30+ models behind one API, from free tiers to frontier |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Access control | Scoped API keys, rotation and usage analytics; enterprise SSO and RBAC options |
| Endpoint roadmap | Audio, images, moderation, batch and fine-tuning are coming soon |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Free tier | Free plan with plugsky-micro and plugsky-lite; 14-day full-access trial |
TL;DR
- Ground automotive answers in approved internal content with RAG instead of model memory.
- Keep unreleased product or supplier content inside VPC, on-prem or air-gapped deployments where policy requires it.
- Scope retrieval per team, client or site so permissions and confidentiality hold at query time.
- Keep a named human owner for every technical or warranty decision.
- Tag bulletins by model and year so answers match the vehicle.
How it works, step by step
- Inventory the content to make searchable: engineering specifications, service bulletins, warranty procedures and supplier documents.
- Define a data boundary for the pilot that excludes restricted material until controls are proven.
- Choose a deployment target: cloud for public content, VPC, on-prem or air-gapped for restricted data.
- Build an evaluation set with service engineers and quality specialists so answer quality is judged by domain experts.
- Ingest, chunk and embed approved documents, and require citations on every answer.
- Add refusal behavior for questions outside the indexed, approved content.
- Review logged interactions on a schedule and expand only after accuracy and access checks pass.
Original data
Try it yourself
Open the embedding model comparison →
Where automotive teams start
Start with internal questions that already have a written answer. High-value first workloads include:
- Engineering specifications: retrieve current specs, drawings notes and change history.
- Service bulletins and warranty: answer repair and warranty questions from bulletins and procedures.
- Supplier and quality documents: surface quality records, supplier audits and corrective actions.
- Dealer support knowledge: answer product and process questions from training and policy documents.
Each use case augments staff with cited answers; none replaces engineering owner judgment.
A private RAG architecture for automotive knowledge
The stack is consistent across industries: ingest approved engineering specifications, service bulletins, warranty procedures and supplier documents, chunk and embed with a multilingual embedding model, store vectors inside your environment, and call chat completions that answer only from retrieved context. Plugsky embeddings and chat completions are OpenAI-compatible, so teams already using OpenAI SDKs change the base URL and keep their code.
Tie every chunk to a part number, program and revision so answers can be verified against the correct drawing or bulletin. Use metadata filters alongside vector similarity rather than relying on similarity alone.
Access control, confidentiality and audit
Supplier contracts, unreleased product data and quality records need separation: filter retrieval by program, plant and supplier, and keep personal or commercial terms out of broad indexes. Automotive obligations vary by market, so map them with counsel and document how the deployment meets them.
The technical controls are consistent: enforce permission-aware retrieval in your own service layer, scope API keys per application, team or tenant, rotate keys, and retain request and response logs on a defined schedule. Regulatory obligations vary by jurisdiction and sector, so map them with counsel rather than assuming one framework covers every deployment; Plugsky supplies the deployment and logging primitives you document.
See the RAG API for related deployment and control detail.
Rollout and human oversight
Pilot on published service and training documentation before touching unreleased product or supplier content. Build a labelled question set with service engineers and quality specialists, then measure retrieval hit rate, citation correctness and answer accuracy before and after every index or model change. Require citations on every answer, refuse out-of-scope questions, and name a human owner for every technical or warranty decision.
Review logged interactions weekly at first, correct the index rather than the prompt when retrieval misses, and expand the corpus only when accuracy and access checks pass.
Honest comparison
| Capability | Plugsky | Public AI assistants | Building in-house |
|---|---|---|---|
| Data boundary | Cloud, VPC, on-prem or air-gapped | Vendor cloud only | You control fully |
| Grounding | Embeddings and RAG are live for engineering, service and quality knowledge search | Uncontrolled retrieval | You assemble and operate |
| Access control | Scoped keys, usage analytics, enterprise SSO and RBAC options | Account-level only | Custom identity work |
| Auditability | Request and response logging | Limited | You build logging |
| Pricing | Flat monthly self-serve plans; see live pricing | Per-seat or per-token | GPU plus operations cost |
| Time to pilot | Days | Hours, without residency control | Quarters |
Frequently asked questions
Can automotive companies keep data private with Plugsky?
Yes. Choose the deployment boundary that matches the data: Plugsky cloud for public content, or your VPC, on-prem and air-gapped options for restricted material. Access is controlled with scoped API keys and usage analytics.
Do we need to fine-tune on our internal documents?
Not for a first release. Fine-tuning is coming soon and is better for style than facts. RAG keeps answers current, permission-aware and traceable to a source, which matters more for internal knowledge.
Which model should we use?
Start free with plugsky-micro and plugsky-lite to validate retrieval, then evaluate mid-tier and frontier models from the 30+ model catalogue on your own question set.
How do we stop wrong or unsupported answers?
Restrict the assistant to approved indexed content, require citations, refuse out-of-scope questions and keep a human decision-maker for every regulated or client-facing outcome.
How is pricing structured?
Self-serve plans are flat monthly with unlimited fair-use usage, and there are no per-token charges on self-serve plans. See the live pricing page for current plans and the free tier.
Can it search service bulletins by vehicle model?
Yes. Tag each bulletin and procedure with model, model year and revision, then filter retrieval so the answer matches the vehicle in front of the technician.
What about supplier-confidential documents?
Keep supplier material in a scoped index with role filters. Commercial terms should not appear in answers available to dealers or partners without explicit approval.