GP Clinic Treatment Protocol & Drug Reference Chatbot
A RAG pipeline ingesting NICE guidelines, BNF drug reference, and a clinic's own patient pathway PDFs into a private vector store. Nursing staff and GPs query by symptom, drug interaction, or pathway step and receive answers that cite the exact source document and page number — no hallucinations, no missing context. No patient data enters the system at any stage.
Discuss a Similar ProjectWhat We Built
Multi-Source Document Ingestion
NICE guidelines, BNF PDFs, and clinic-specific patient pathway documents parsed, chunked, embedded, and stored in a private pgvector instance — updated automatically when source documents change.
Cited-Answer RAG Pipeline
Every response includes the source document name, section title, and page number inline. Clinicians can trace every answer back to its authoritative source in one click.
Clinical Safety Guardrails
Queries outside the ingested knowledge scope are flagged clearly and routed to a human review queue — the system never speculates beyond its verified source material.
Role-Based Access Control
GPs, nurses, and reception staff each see a different scope of queryable content — controlled per role, with no crossover into content outside their access level.
Full Clinical Audit Log
Every query and response is stored with a timestamp, user role, and source citations — providing a complete record for clinical governance and CQC inspection readiness.
Clinic-Own Cloud Deployment
Deployed entirely within the clinic's own cloud account. No data shared with third parties. Patient data never enters the system — the chatbot operates solely on clinical reference material.
Technologies Used
Key Outcomes
Reduction in time spent looking up treatment protocols and drug references
Hallucinated answers — every response cites the exact source document and page
From kickoff to live deployment on the clinic's own cloud infrastructure
Need Something Similar?
Tell us about your knowledge sources, compliance requirements, and team structure. We will design a RAG pipeline that fits your exact context.