Key facts
| API surface | OpenAI-compatible /v1/chat/completions; keep your existing SDK |
| Grounding | Embeddings and RAG are live for SOP, study and regulatory document 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 life sciences answers in approved internal content with RAG instead of model memory.
- Keep study or regulated-record 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 regulated or clinical-trial decision.
- Keep records of truth in your validated quality systems.
How it works, step by step
- Inventory the content to make searchable: SOPs, study reports, regulatory dossiers and safety documentation.
- 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 quality and regulatory reviewers 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 life sciences teams start
Start with internal questions that already have a written answer. High-value first workloads include:
- SOP and validation docs: answer from controlled procedures and validation records.
- Study and clinical reports: retrieve study summaries and protocol content.
- Regulatory dossiers: answer from submissions and correspondence with citations.
- Safety documentation: surface case handling procedures and reference material.
Each use case augments staff with cited answers; none replaces qualified reviewer judgment.
A private RAG architecture for life sciences knowledge
The stack is consistent across industries: ingest approved SOPs, study reports, regulatory dossiers and safety documentation, 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.
Document status and version belong in metadata: index only approved or current documents, exclude drafts from default retrieval, and keep an audit trail. Use a reranker on long dossiers and validation records.
Access control, confidentiality and audit
Controlled documents, study data and submission correspondence require strict access and version control. Filter retrieval by role and document status, keep regulated records under your existing quality system, and treat model output as unreviewed draft material until a qualified person approves it.
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 AI vendor risk assessment for related deployment and control detail.
Rollout and human oversight
Pilot on published SOPs and training material before touching study or regulated-record content. Build a labelled question set with quality and regulatory reviewers, 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 regulated or clinical-trial 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 SOP, study and regulatory document 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 life sciences teams 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 replace our quality documentation systems?
No. It sits alongside your controlled document and quality systems, retrieving approved content. Records of truth stay in your validated systems.
How do we handle documents under change control?
Index only approved versions, exclude drafts from default retrieval, and re-embed when a document is revised.