RAG API for healthcare
RAG API for Healthcare | Ragex
Build grounded healthcare AI with a RAG API for healthcare. Parse clinical PDFs, scanned forms, and formularies with tenant isolation and encrypted storage.
Ragex gives development teams a managed retrieval pipeline that parses clinical PDFs, scanned forms, and drug formularies — then makes them searchable through natural language queries. Instead of assembling your own document processing stack, you get grounded AI responses backed by your institution's actual clinical documentation.
Healthcare AI Challenges
Clinical staff spend an estimated 34% of their working time searching for information across fragmented systems — EHRs, policy manuals, drug databases, and institutional protocols. When AI tools are introduced to reduce that burden, they face a problem unique to medicine: hallucinated information is not just unhelpful, it is dangerous.
A chatbot that invents a drug dosage or fabricates a contraindication creates immediate liability risk. In healthcare, retrieval grounding is not an optimization — it is a safety requirement. RAG addresses this by anchoring every LLM response in actual clinical documentation rather than training data.
Healthcare documents are also structurally complex. Scanned PDFs, multi-column clinical papers, tables with lab values, and images of test results all need to be searchable. Building a pipeline that handles all of it means choosing an embedding model, a vector database, a document parser, a reranker, and a chunking strategy — each with its own vendor and failure mode. Ragex eliminates that burden so your team can focus on building clinical chatbots and document search tools instead of debugging infrastructure.
Real-World Healthcare Use Cases
Clinical Decision Support
A hospitalist needs to check institutional treatment guidelines while admitting a patient. Instead of navigating three systems, they type a natural language query. The system retrieves the relevant guideline section from your uploaded clinical protocols with source attribution. The LLM generates an answer grounded entirely in that evidence — no hallucination. This is the customer support pattern adapted for clinical settings where the stakes are higher.
Patient-Facing Health Q&A
A health system builds a patient portal where users ask about their condition or medications. Responses come from your institution's approved patient education materials — not the internet. When a patient asks "what are the side effects of metformin?", they get an answer sourced from your formulary. Build this as a standalone feature or integrate using LangChain or LlamaIndex.
Internal Knowledge Search
Your compliance team manages hundreds of policy documents and regulatory guidelines. Today, finding the right policy means keyword searching across a shared drive. With Ragex, they search semantically: "infection control procedures for ICU visitors" finds your "Hand Hygiene and PPE Requirements" document even without shared keywords. This is the same pattern behind internal knowledge base search, tuned for healthcare's regulatory density.
Your Healthcare Documents, Ready to Go
The API handles 16 file types out of the box, with advanced parsing for the formats that matter most in clinical settings:
Parsed with layout-aware extraction: PDF (including scanned pages via OCR), DOCX (policy documents), PPTX (training materials), XLSX (formulary tables and lab reference ranges), and PNG/JPG/TIFF (scanned documents and test results).
Direct text ingestion: TXT, MD, HTML (web-based clinical guidelines), CSV (drug databases, ICD-10 code lookups), and JSON (FHIR resources and structured clinical data).
Tables are preserved as single chunks — the system never splits mid-table. A drug interaction matrix or lab reference range table is always retrieved as a complete unit, because a partial table in healthcare can omit critical safety information. If you are evaluating retrieval platforms, our Pinecone alternatives guide explains how document handling differs across providers.
Security and Compliance
Healthcare data requires strict access controls. The API implements multiple layers of technical safeguards:
Tenant isolation: Every request is scoped by account_id at both the application and database layers. No cross-tenant data leakage — the same architecture that supports multi-tenant chatbot systems and customer support tools.
Encryption: All traffic over HTTPS. Documents stored with private containers and short-lived access tokens. API keys hashed at rest — the raw key is shown once at creation and never stored.
Access controls: Bearer token authentication on every endpoint. Rate limiting per plan. Structured audit logging for compliance reviews.
HIPAA status: The current version implements technical safeguards aligned with HIPAA requirements — encryption, tenant isolation, access controls, and audit logging. However, a formal Business Associate Agreement (BAA) and HIPAA compliance audit are on the product roadway and not yet available. For workloads containing Protected Health Information (PHI), discuss your specific compliance requirements with our team before deploying to production.
Who This Is For
Healthcare CTOs evaluating how to add AI capabilities without compliance risk or a multi-vendor integration project. One vendor, one API, no vector database to operate. See how this compares to building with Pinecone.
Developers building clinical tools who want to focus on the application layer — the chatbot UI, the clinician workflow, the patient portal — not retrieval infrastructure. Works with any LLM and integrates with Vercel AI SDK and LangChain.
Compliance leads who need to understand what data goes where. Tenant isolation, encrypted storage, and audit logging provide the controls you need for internal review.
How to Get Started
The fastest path is a focused pilot: one department, one document set, one use case.
- Pick a document set: Start with your department's clinical guidelines, a drug formulary, or a set of policy documents.
- Upload and index: Parsing, chunking, and indexing happen automatically. No embedding model to choose, no vector database to configure.
- Query and evaluate: Run real clinical queries against your documents. Verify that retrieved context is accurate and complete.
- Build the interface: Connect the retrieval API to your LLM — whether that is a document search interface, a clinical chatbot, or an internal Q&A tool.
Most teams go from zero to a working prototype in under a day.
Pricing for Healthcare
Healthcare knowledge bases tend to be large — clinical guidelines, formularies, and policy manuals add up quickly.
| Plan | Monthly | Pages | Queries | Typical Healthcare Fit |
|---|---|---|---|---|
| Starter | $29 | 500 | 5,000 | Small clinic pilot, single department |
| Pro | $79 | 2,000 | 15,000 | Small hospital department |
| Business | $229 | 6,500 | 50,000 | Hospital department, growing knowledge base |
| Scale | $499 | 15,000 | 120,000 | Multi-department, large formulary and guidelines |
Business or Scale is typically the right starting point. For a cost comparison across providers, our Pinecone comparison covers total cost of ownership including developer time.
FAQ
Is this API HIPAA compliant?
The current version implements technical safeguards aligned with HIPAA: tenant isolation, encryption in transit and at rest, access controls, and audit logging. However, a formal BAA and HIPAA compliance audit are not yet complete. For workloads containing PHI, discuss your specific compliance requirements with our team before deploying to production.
Can it handle scanned clinical documents?
Yes. The API parses scanned PDFs via OCR, along with PNG, JPG, and TIFF image files. The parsing layer uses layout-aware extraction to handle handwritten notes, multi-column clinical papers, and complex document structures common in healthcare settings.
How does it handle medical tables and structured data?
Tables are preserved as single chunks and never split mid-row or mid-column. A lab reference range table or drug interaction matrix is always retrieved as a complete unit. CSV and XLSX files are also supported for structured data like ICD code lookups and formulary databases.
How does this differ from building a custom retrieval pipeline?
Building your own pipeline means selecting at least five components: an embedding model, a vector database, a document parser, a reranker, and a chunking strategy. Each has its own vendor, configuration, and maintenance burden. A managed API handles all of that. When better components become available, your retrieval quality improves without a code change. For a detailed breakdown, see our Ragex vs Pinecone comparison.
Last updated: February 20, 2026