The Retrieval Infrastructure for Healthcare AI
ZeroEntropy gives developers the models and infrastructure to build accurate search and RAG over medical data: clinical notes, research papers, formularies, and regulatory filings. One API for both human-facing search and AI agent context.
Generic Retrieval Breaks on Medical Data
Medical records span thousands of pages across disconnected systems: clinical notes, discharge summaries, transcripts, lab results, imaging reports, research papers, and clinical guidelines scattered across EHRs, publication databases, and internal file systems. Too much context for humans to review manually, and too messy for off-the-shelf embeddings to retrieve accurately. Your users and AI agents both suffer.
End-to-end latency (embed + rerank).
Than any API-based models.
“Understands medical terminology better than anything we’ve tested. Our team finds relevant clinical trial data in seconds, not hours.”
“We integrated ZeroEntropy into our EHR workflow. Clinicians surface relevant patient history and guidelines without leaving their screen.”
“Semantic search across PubMed and internal papers cut our literature review from weeks to hours. Clear accuracy lift over everything else we tried.”
Retrieval That Understands Medicine
ZeroEntropy surfaces the exact study, guideline, or clinical note your team needs — with semantic understanding of medical terminology, drug interactions, and diagnostic criteria.
Clinical Decision Support
Surface relevant guidelines, drug interactions, and evidence-based recommendations in real time.
Medical Research
Semantic retrieval across papers, trial results, and systematic reviews beyond keyword matching.
Patient Record Retrieval
Find relevant notes, labs, and imaging reports across fragmented EHR systems with natural language.
Regulatory & Compliance
Retrieve FDA submissions, IRB protocols, and audit documentation instantly.
Integrate ZeroEntropy in minutes. Models only, or end-to-end retrieval. Production-ready.
# Create an API Key at https://dashboard.zeroentropy.dev
from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()
response = zclient.models.rerank(
model="zerank-2",
query="What is Retrieval Augmented Generation?",
documents=[
"RAG combines retrieval with generation...",
],
)
for doc in response.results:
print(doc)Deploy in your own cloud with dedicated infrastructure. Available on AWS Marketplace and Azure.
From security to scale, ZeroEntropy is built for the demands of production ready AI

SOC2 Type II
Audited controls for data security, availability, and confidentiality — verified annually.

HIPAA Compliant
BAA-ready infrastructure with encryption at rest and in transit for protected health data.

GDPR Compliant
Full data residency controls, right-to-deletion, and DPA agreements for EU customers.

CCPA Compliant
Consumer data rights honored with full transparency on collection, use, and deletion.
