Key facts
| Access control | Scoped API keys with rotation; enterprise SSO and RBAC options |
| Deployment | Cloud, VPC, on-prem or air-gapped to separate business units |
| Auditability | Request, model and response logging for engineering review |
| Data grounding | Embeddings and RAG are live for standards, service manuals and test reports |
| Structured output | JSON mode returns defect, requirement and test records in fixed schemas |
| Models | 30+ models behind one API for engineering and service workloads |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Endpoint roadmap | Images, audio, files, batch and fine-tuning are coming soon |
TL;DR
- Keep vehicle telemetry and location data inside private deployments.
- Separate engineering IP from service and dealer workloads by key and boundary.
- Ground answers in standards and service manuals with citations.
- Give supplier and partner integrations their own scoped credentials.
- Log model interactions so design reviews and recalls can be reconstructed.
How it works, step by step
- Map AI workflows across engineering, manufacturing, service and connected-vehicle platforms.
- Classify data: public standards, internal engineering IP, production telemetry, customer data.
- Choose deployment per class, using VPC, on-prem or air-gapped for IP and vehicle data.
- Issue per-application and per-partner keys, with rotation and a central inventory.
- Index standards, manuals and test reports, and require citations on engineering answers.
- Define review gates for anything affecting design decisions, service instructions or safety.
- Log requests, sources, model versions and approvals, and review access quarterly.
Try it yourself
Open the AI API key security checklist →
Connected-vehicle data and privacy
Modern vehicles emit location, diagnostic and usage telemetry. Combine a few signals and you can infer behavior, which makes this data sensitive regardless of jurisdiction. Keep telemetry workflows inside private deployments, aggregate or de-identify before the model sees data, and document the path from vehicle to answer so privacy reviews have evidence.
Service-bay assistants should query a vehicle record through a permissioned tool rather than accepting free-text identifiers in prompts.
Design, production and supplier IP
CAD notes, test results, production parameters and supplier terms are competitive assets. Treat any workflow that touches them as an IP boundary: separate keys from marketing or service use cases, run the workflow on a private deployment when required, and keep retrieval indexes scoped to the teams allowed to see the content.
Supplier integrations get their own credentials and their own logging, so partner activity is attributable.
Keys, identity and partner access
Automotive supply chains involve many external parties. Issue a distinct key per partner integration, store secrets centrally, and rotate them on contract milestones or staff changes. Enterprise SSO and RBAC options cover console access for internal teams, so permissions track the organization chart rather than shared credentials.
Review key inventories quarterly; dormant partner keys are a common finding in audits.
Engineering governance and safety review
Require citations for any answer drawn from standards, manuals or test reports, and keep a named reviewer for outputs that influence design, service instructions or safety analysis. Use JSON mode so defect, requirement and test records land in schemas your engineering systems already validate. Keep an approved model list with evaluation evidence, and re-test on every model change.
Honest comparison
| Control area | Plugsky capability | Common gap | Owner |
|---|---|---|---|
| Vehicle data | VPC, on-prem, air-gapped deployment options | Telemetry in shared chat tools | Privacy and product security |
| Engineering IP | Per-key isolation and private deployment | One key for all departments | Engineering IT |
| Supplier access | Per-partner keys and usage analytics | Unmanaged partner credentials | Procurement |
| Audit trail | Request, source and model logging | No reconstruction path | Quality and compliance |
| Model governance | Approved model list behind one API | Uncontrolled model changes | Engineering governance |
| Safety review | Citations plus structured output for review | AI text used unreviewed | Functional safety lead |
Frequently asked questions
Can connected-vehicle data be processed at all?
Yes, under strict conditions: private deployment, data minimization, aggregation or de-identification, and a documented path from source to answer that your privacy review approves.
How do we separate engineering from service workloads?
Use distinct keys and, where required, distinct deployments. Retrieval indexes should be scoped to the teams permitted to see the underlying content.
What should we log?
Request IDs, model names and versions, retrieved sources, outputs and reviewer decisions. That supports design reviews, service disputes and recall investigations.
Is image or drawing analysis available?
Image endpoints are coming soon. Today, extract text from drawings and reports upstream, then query the extracted content through the live chat and embedding APIs.
How do we evaluate engineering answers?
Build a labelled set from standards and test reports, measure retrieval accuracy and citation correctness, and repeat the evaluation on every model or prompt change.
Can suppliers use the same platform safely?
Yes, with per-partner keys, their own logging, and access limited to the content their agreement covers. Avoid shared credentials across suppliers.
Where do we start?
Pilot on public standards and internal service manuals with the free plan, confirm citations and logging, then extend to sensitive IP on a private deployment.