How can pharmaceutical companies 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 pharmaceutical 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 SOPs, batch records and regulatory dossiers behind a cited RAG assistant.
• Deploy in your VPC, on-prem or air-gapped so batch, quality and submission data stays inside your boundary.
• Embeddings and RAG are live today; audio and image endpoints are on the roadmap.
• Function calling lets agents read quality and document 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: standard operating procedures, batch records, validation protocols, regulatory dossiers and clinical study reports.
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 quality and document 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 RAG sandbox →

Where pharmaceutical teams get value first

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

• Procedure search: find the current SOP, revision history and training requirement for a task with citations.
• Deviation investigations: pull prior deviations, root-cause notes and corrective actions for similar events.
• Regulatory dossier Q&A: query submission content and agency correspondence without opening dozens of documents.
• Training and onboarding: answer role-based questions from approved training material.

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 quality, training and document-management systems through your own APIs, with the model choosing the call and your service enforcing permissions.

Residency, access and auditability

Pharmaceutical 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 qualification and validation decisions with accountable human owners; the assistant drafts and cites, it does not approve.

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 an accountable human owner for every quality decision; the assistant drafts and cites, it does not approve. Keep every answer traceable to a source.

Honest comparison

Capability	Plugsky	Generic chatbot	Building in-house
Pharmaceutical 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 quality and document 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 SOPs and long regulatory dossiers?

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 this support regulated quality workflows?

RAG runs over your approved, version-controlled documents and returns citations, but pair it with your existing validation, change-control and record-retention processes rather than replacing them.

What about handwritten or scanned batch records?

Vision and OCR endpoints are coming soon. Until then, extract text with your existing OCR pipeline, embed that text, and retain the scan as the source of record.