Asclevor for Pharma & Life Sciences
Evidence intelligence across therapeutic areas.
Explore the published clinical literature programmatically — retrieve comparable patient cases at scale and build on a structured, cross-linked medical ontology.
import requests
response = requests.post(
"https://api.asclevor.com/v1/search",
json={
"query": "hepatic adverse events during immune checkpoint inhibitor therapy",
"limit": 25
},
){
"query": "hepatic adverse events during immune checkpoint inhibitor therapy",
"model": "bge-small-en-v1.5",
"count": 25,
"results": [
{ "pmid": "31784915", "score": 0.91 },
{ "pmid": "33748325", "score": 0.87 },
{ "pmid": "32409471", "score": 0.83 }
]
}The evidence exists. It doesn't organize itself.
Published medical knowledge keeps growing across thousands of journals and document types. For research teams, the bottleneck is not access — it is retrieval that understands the medicine.
- Scale without structure
- Millions of publications, unstructured text, and terminology that shifts across therapeutic areas.
- Keyword triage
- String matching cannot express a clinical presentation, a course, or a population.
- Siloed knowledge
- Evidence, cases, and concept relationships live in different tools and different formats.
- Integration cost
- Insights only matter when they reach the models, pipelines, and teams that use them.
How Asclevor helps
A knowledge layer for medical research.
Case retrieval at scale
Query by clinical meaning and retrieve comparable published cases across the literature.
Cross-linked ontology
700+ conditions and 300+ symptoms with differentials, comorbidities, severity scores, and ICD-10 mappings.
Structured by default
One REST API, structured JSON, and SDKs for TypeScript and Python.
Verifiable outputs
Similarity scores and source PMIDs on every retrieved case.
Use cases
Where research teams put Asclevor to work.
Evidence
Evidence landscaping
Map published cases and concepts around a therapeutic area or mechanism.
Development
Clinical development research
Explore how presentations and courses appear across the published literature.
Data science
Knowledge pipelines
Pipe semantic retrieval into internal models, analyses, and tooling.
Medical affairs
Scientific landscape reviews
Support literature exploration with case-level, citable retrieval.
Asclevor supports knowledge discovery. It does not diagnose, recommend treatment, or replace clinical judgment.
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