How can research teams use RAG for internal knowledge?

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 research 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 papers, protocols and grant reports behind a cited RAG assistant.
• Deploy in your VPC, on-prem or air-gapped so unpublished research data stays inside your boundary.
• Embeddings and RAG are live today; audio and image endpoints are on the roadmap.
• Function calling lets agents read repository and grant systems on request.
• Evaluate on the free plan, then move to a private deployment for production.

How it works, step by step

1. Inventory document sources: papers and preprints, lab protocols, grant applications, datasets and internal review notes.
2. Classify content by sensitivity and decide what may leave an on-prem or air-gapped boundary.
3. Create a Plugsky account and API key on the free plan, or request a private deployment.
4. Chunk and embed approved documents, keeping the vector store inside your own environment if required.
5. Wire retrieval into an assistant that cites sources and refuses out-of-scope questions.
6. Add function calling for repository and grant systems once retrieval is trusted.
7. Log queries and answers, review them weekly, and expand coverage document class by document class.

Original data

Try it yourself

Open the local RAG stack generator →

Where research teams get value first

Start with document-heavy work that already has an owner and an approval path:

• Literature and precedent search: find prior methods, results and citations across your corpus.
• Protocol lookup: answer method questions from current protocols with version citations.
• Grant and report drafting: pull prior text, budget notes and reviewer feedback.
• Institutional knowledge: keep documented know-how searchable after researchers move on.

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 lab information, repository and grant systems through your own APIs, with the model choosing the call and your service enforcing permissions.

Residency, access and auditability

Research 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. Keep unpublished data inside your boundary, scope access by project, and preserve citation trails for reproducibility.

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. Keep unpublished data inside your boundary, scope access by project, and preserve citation trails for reproducibility. Keep every answer traceable to a source.

Honest comparison

Capability	Plugsky	Generic chatbot	Building in-house
Research 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 repository and grant 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 papers, protocols and datasets?

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 unpublished research stay private?

Yes. Deploy in your VPC, on-prem or air-gapped and keep embeddings there; access is limited to the project members and API keys you authorize.

Do you support figures, images and slides?

Vision and image endpoints are coming soon. Text extraction covers papers and reports today; keep figure files linked so readers can verify context.