Healthcare

Clinical text is among the most sensitive data there is, and hospitals often cannot send it to external AI services.

The challenge

• Clinicians spend time searching notes, guidelines and discharge summaries.
• Similar past cases are hard to find by keyword alone.
• Patient data must stay inside the hospital’s own systems.

Why SrvDB fits

• Guideline and protocol RAG: answer questions from internal guidelines with a local LLM.
• Similar-case search: find de-identified past cases that resemble a current one.
• On-premises only: data stays in a local directory on hospital hardware.

Five projects, five levels

Each project has its own page: the problem, how SrvDB powers it, what changes, architecture, use cases, core code and limits.

Level	Project	What it does
1 · Beginner	Protocol Assistant	Answers clinicians’ questions from the hospital’s own protocols and formulary, with citations.
2 · Intermediate	Similar Case Finder	Finds de-identified past cases that resemble the patient in front of you.
3 · Advanced	Trial Matcher	Shortlists patients for clinical trials by comparing summaries with eligibility criteria.
4 · Expert	Pharmacovigilance Monitor	Matches new adverse-event reports to known safety signals and groups similar reports.
5 · Frontier	On-prem Clinical AI Stack	A complete hospital AI stack with zero data egress: local embeddings, local LLM, SrvDB.

Caution

SrvDB keeps data local, but it is not a certified medical device or a compliance product. Regulatory compliance (HIPAA, GDPR and similar) remains the deployer’s responsibility.

Coming soon

Runnable reference implementations of these projects are in progress.