Authentication
All API requests require a valid session token obtained via the VetIOS authentication surface. The platform uses Supabase-based JWT sessions. Include your token in the Authorization header:
Authorization: Bearer <your-session-token> Content-Type: application/json
Tokens are tenant-scoped. An operator token cannot access another tenant's data. Token expiry and rotation are managed automatically by the platform session layer.
API Reference
/api/inferenceCoreSubmit a clinical case for AI-assisted differential diagnosis. Returns ranked hypotheses with confidence scores, CIRE signals, and a traceable inference event ID.
{
"model": { "name": "VetIOS Diagnostics", "version": "latest" },
"input": {
"input_signature": {
"species": "canine",
"breed": "mixed",
"symptoms": ["vomiting", "lethargy"],
"metadata": {
"age_years": 3,
"labs": { "wbc": 4.1, "pcv": 29 }
}
}
}
}{
"inference_event_id": "9f2c1b6a-...",
"data": {
"confidence_score": 0.82,
"differentials": [
{ "label": "canine_parvovirus", "p": 0.82 },
{ "label": "hemorrhagic_gastroenteritis", "p": 0.41 }
]
},
"cire": { "phi_hat": 0.71, "cps": 0.12, "safety_state": "nominal" },
"meta": { "tenant_id": "...", "request_id": "..." },
"error": null
}/api/outcomeLearningSubmit a confirmed outcome for a resolved case. Outcome events are used by the closed-loop learning layer to refine inference priors within your tenant.
{
"inference_event_id": "9f2c1b6a-...",
"outcome": {
"type": "confirmed_diagnosis",
"payload": {
"label": "canine_parvovirus",
"confidence": 0.98
},
"timestamp": "2026-05-04T12:00:00.000Z"
}
}{
"outcome_event_id": "evt_2841...",
"clinical_case_id": "case_4XK3...",
"linked_inference_event_id": "9f2c1b6a-...",
"request_id": "..."
}/api/simulateSimulationRun a synthetic simulation against a base case to pressure-test model behaviour before rollout. Returns a stability report and simulation trace.
{
"steps": 10,
"mode": "adaptive",
"base_case": {
"species": "canine",
"symptoms": ["vomiting", "lethargy"],
"metadata": { "wbc": 4.1, "pcv": 29 }
},
"inference": {
"model": "gpt-4o-mini",
"model_version": "gpt-4o-mini"
}
}{
"simulation_event_id": "sim_901A...",
"clinical_case_id": "...",
"stability_report": {
"passes": 9,
"failures": 1,
"mean_confidence": 0.79
},
"request_id": "..."
}Core Concepts
Inference Engine
The core runtime that normalises clinical inputs, routes them through the model layer, and returns ranked differential hypotheses with confidence scores and CIRE signals.
CIRE Signals
Clinical Inference Reliability Estimation. Each inference response includes phi_hat (hypothesis confidence), cps (calibration pressure score), and safety_state — giving operators visibility into output reliability.
Outcome Learning
Closed cases submitted via /api/outcome become supervision events. The learning layer uses confirmed diagnoses to refine inference priors within your tenant's isolated partition.
Simulation Layer
Run synthetic case traffic against new model versions or policy changes before promoting them to live inference. Validates stability across edge cases and replayed historical patterns.
Tenant Isolation
All clinical data, inference history, and outcome learning are partitioned per tenant. Data does not cross tenant boundaries. Operators within a tenant control access roles.
Traceable Runtime
Every inference event produces a persistent inference_event_id. All requests, model calls, latency spans, and policy checks are logged to an immutable audit trail.
Species Support
The inference engine accepts species-typed inputs. Correct species specification improves differential ranking accuracy as physiological priors are species-specific.
Rate Limits
Rate limit headers are returned on every response: X-RateLimit-Remaining and X-RateLimit-Reset.
Ready to integrate?
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