For AI / ML Engineers
Your models deserve
typed evidence,
not raw records.
OpenBio provides a normalized, governed evidence substrate with 16 assertion types and 6 trust layers. No custom ETL. No document parsing. Just typed evidence, ready to query.
// Typed evidence, filtered by trust layer
await openBio.searchEvidence('james-001', {
trustLayer: 'source_fact'
})
// → 142 assertions · all provenance-tagged// Submit annotation — never overwrites source facts
await openBio.submitAnnotation('assertion-789', {
type: 'hypothesis', confidence: 0.72
})
// → source_fact unchanged · provenance preservedThe ETL Tax
Every AI team building on clinical data writes the same pipeline.
Inconsistent schemas across source systems
FHIR, HL7, CSV, VCF — each requires custom parsing. Your models can’t be trained on data that doesn’t share a schema.
No trust metadata
Your model can’t distinguish a source fact from a model output. It treats an AI prediction with the same confidence as a lab result.
Overwrites destroy provenance
When annotations overwrite source data, you lose the audit trail. Six months later, no one knows what the original reading was.
Normalized. Typed. Governed.
The evidence substrate your models have been waiting for.
16 Assertion Types
diagnosis, lab_result, variant_call, imaging_finding, medication, procedure, and 10 more. Typed schemas. No ambiguity.
6 Trust Layers
source_fact through hypothesis. Your model always knows the epistemic status of its inputs — never confuses prediction with observation.
MCP Compatible
Query the evidence substrate via the Model Context Protocol. Works with any agent framework. Structured tools, typed responses.
No ETL Required
Ingest once, query typed assertions forever. OpenBio handles normalization, deduplication, and provenance tracking.
Every assertion carries its
epistemic status.
Source Fact
Raw data directly from a source system. Highest confidence.
Normalized
Standardized and mapped to canonical terminology.
Extracted
Derived from unstructured documents or images via NLP/CV.
Derived
Computed from other evidence assertions.
Model Output
AI/ML prediction. Confidence score required.
Hypothesis
Proposed interpretation. Under review.
Agent Access
Three tools. Every assertion in the substrate.
OpenBio exposes the evidence substrate through a tight MCP-compatible tool surface. Search with filters, submit annotations that layer above source data, and build cross-species cohorts — all without touching raw records.
searchEvidence()submitAnnotation()buildCohort()
// Typed evidence, filtered by trust layer await openBio.searchEvidence('james-001', { assertionTypes: ['variant_call', 'lab_result'], trustLayer: 'source_fact' }) // → 142 assertions · all provenance-tagged
// Submit annotation — never overwrites source facts await openBio.submitAnnotation('assertion-789', { type: 'hypothesis', confidence: 0.72, rationale: 'Consistent with EGFR pathway' }) // → source_fact unchanged · provenance preserved
// Build a cohort across species await openBio.buildCohort({ criteria: { assertion: 'EGFR_exon19_del'}, subjectTypes: ['human', 'primate'] }) // → CohortResult · 3 subjects matched