Key facts
| API surface | OpenAI-compatible /v1/chat/completions; change base_url and model name |
| Models | 30+ models including long-context and multilingual options |
| Retrieval | Embeddings and RAG are live for oil and gas document search with citations |
| Agents | Function calling and agent orchestration are live |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Data residency | Region selection and sovereign deployment options |
| Free tier | Free plan with 2 free AI models (plugsky-micro, plugsky-lite), no card |
| Endpoint roadmap | Audio, images, moderation, batch and fine-tuning are coming soon |
TL;DR
- Put drilling reports, well logs and HSE procedures behind a cited RAG assistant.
- Deploy in your VPC, on-prem or air-gapped so reservoir, safety and contract data stays inside your boundary.
- Embeddings and RAG are live today; audio and image endpoints are on the roadmap.
- Function calling lets agents read maintenance and well-data systems on request.
- Evaluate on the free plan, then move to a private deployment for production.
How it works, step by step
- Inventory document sources: drilling and completion reports, well logs, HSE procedures, maintenance records and contracts.
- Classify content by sensitivity and decide what may leave an on-prem or air-gapped boundary.
- Create a Plugsky account and API key on the free plan, or request a private deployment.
- Chunk and embed approved documents, keeping the vector store inside your own environment if required.
- Wire retrieval into an assistant that cites sources and refuses out-of-scope questions.
- Add function calling for maintenance and well-data systems once retrieval is trusted.
- Log queries and answers, review them weekly, and expand coverage document class by document class.
Original data
Try it yourself
Open the RAG architecture builder →
Where oil and gas teams get value first
Start with document-heavy work that already has an owner and an approval path:
- Well operations troubleshooting: search offset-well histories, drilling reports and completion notes with citations before changing a program.
- HSE and operations Q&A: ask against procedures, permits and incident reports instead of hunting through shared drives.
- Maintenance and integrity: trace equipment history across assets, service reports and inspection findings.
- Contracts and JV knowledge: find clauses across joint-venture agreements, tenders and vendor terms.
Each use case is retrieval-first: the model answers from your documents and shows where the answer came from.
Architecture: retrieval first, agents second
A workable stack is small: an ingestion pipeline that chunks and embeds approved documents, a vector store you control, and a chat call that receives the top matches as context. Plugsky embeddings and chat completions are OpenAI-compatible, so the glue code is familiar to any team that has used the OpenAI SDK.
Add function calling only after retrieval is trustworthy. Agents can then request data from maintenance, procurement and well-data systems through your own APIs, with the model choosing the call and your service enforcing permissions.
Residency, access and auditability
Oil and gas data is sensitive. Plugsky supports cloud, VPC, on-prem and air-gapped deployments, with region selection for residency requirements. Air-gapped installations keep prompts and documents inside your network entirely.
Use scoped API keys per application, rotate them on a schedule, and log requests and responses under your retention policy. For safety-critical answers, require citations and a human review step before anything is acted on.
A rollout plan that stays explainable
Pick one document class, build a fifty-question evaluation set with known answers, and measure retrieval hit rate and answer accuracy before expanding. Publish the assistant to one team, collect the questions it could not answer, and close those gaps by adding documents rather than prompts. Review high-impact answers with a qualified owner before they influence field or financial decisions. Keep every answer traceable to a source.
Honest comparison
| Capability | Plugsky | Generic chatbot | Building in-house |
|---|---|---|---|
| Oil and gas document search | RAG with citations over your corpus | No access to internal documents | You build ingestion and evaluation |
| Deployment | Cloud, VPC, on-prem, air-gapped | Vendor cloud only | Your infrastructure |
| Model choice | 30+ models behind one API | Single vendor model | You host each model |
| Function calling | Live for maintenance and well-data systems | Limited or unavailable | Custom integration work |
| Pricing | Flat monthly self-serve plans; see live pricing | Per-seat subscription | GPU plus operations cost |
| Audio and image endpoints | Coming soon | Varies by vendor | Separate pipelines to maintain |
Frequently asked questions
Can our data stay inside our own network?
Yes. Plugsky supports VPC, on-prem and air-gapped deployments, so prompts, documents and embeddings remain inside your environment when required.
Does Plugsky train on our documents?
For strict requirements, choose a private or air-gapped deployment so content stays inside your environment. Review the current data-handling terms and DPA for cloud plans before rollout.
How do citations work?
Your retrieval layer passes the top matching chunks into the chat call, and the assistant returns answers with references to those chunks. Require a refusal when no source clears the relevance threshold.
Which model should we use for large technical manuals and well reports?
Benchmark a long-context model against chunked RAG on your own questions. Long-context helps with whole-document reasoning; RAG is cheaper and easier to cite.
Is there a free way to evaluate this?
Yes. The free plan includes two free AI models with no card, and the 14-day full-access trial lets you test larger models on your documents.
Can we keep well and reservoir data on-prem?
Yes. Plugsky supports VPC, on-prem and air-gapped deployments, so documents, embeddings and prompts stay inside your network or field infrastructure.
Can the assistant work with scanned field documents?
Dedicated vision and OCR endpoints are coming soon. Today, run OCR upstream, embed the extracted text, and link back to the original scan so engineers can verify it.