The outcome-confirmed data layer for veterinary AI.
VetIOS captures the scarce layer every veterinary model needs: de-identified clinical evidence linked to provenance, clinician review, lab context, follow-up, and trust scores.
The interface is visible. The evidence ledger is the asset.
3 year mixed-breed dog with acute vomiting, lethargy, dehydration, leukopenia, low PCV, and recent shelter exposure.
One runtime. Five compounding stages.
VetIOS operates as a compounding intelligence loop, not a static model.
De-identified clinical signals enter with consent scope, source lineage, and policy state attached.
Models produce ranked hypotheses with confidence bands, citations, runtime traces, and review gates.
Diagnoses, treatments, follow-ups, labs, and specialist review return as scarce supervisory evidence.
Partner nodes contribute only eligible, outcome-confirmed, provenance-scored evidence into learning rounds.
Candidates advance only when benchmark, safety, drift, calibration, and rollback evidence clears governance.
A field note inside the control plane.
The video is context, not the product. VetIOS should feel like the clinical data substrate underneath every visible interface: provenance-aware, outcome-linked, and operational before it is theatrical.
Platform modules for the entire clinical loop
Each layer is designed as infrastructure: typed inputs, observable execution, and system-level feedback.
Provenance Substrate
Every usable learning record carries consent posture, source lineage, de-identification state, outcome linkage, and a trust score.
Outcome Learning Plane
Closed cases become governed supervision events only after clinician, lab, specialist, or follow-up confirmation is captured.
Federated Promotion Controls
Partner-node updates, benchmark packets, model cards, rollout monitors, and rollback decisions stay tied to evidence hashes.
The system gets stronger because the loop is the product.
Every interaction strengthens the system.
Outcome-confirmed intelligence, not token volume
VetIOS reports distinct, non-synthetic inferences closed by a clinician-reviewed or laboratory-confirmed label. Repeated outcome rows, inferred-only labels, and simulation traffic do not inflate this metric.
0 imported through the real-case path.
0 carry clinician review; 0 carry lab confirmation. Synthetic traffic is excluded.
Awaiting 200 real clinician/lab-confirmed pairs before reliability claims are evidence-grade.
2 PIMS packs and 5 passive event types are defined.
4 reviewable CDS drafts and 84 human-review routes recorded.
0 culture-guided stewardship events and 0 outcome-tracked events.
0 completed reviews and 0 learning-eligible oversight signals.
What is real right now
Consent-gated, de-identified case rows can enter the dataset API.
Inference events carry prompt, schema, model, and CIRE lineage.
Clinic workflow events normalize into passive signal contracts.
0 distinct confirmed inferences; 0 synthetic inference paths excluded; 0 carry calibration deltas.
Awaiting 200 real clinician/lab-confirmed pairs before reliability claims are evidence-grade.
Append-only review events can capture specialist disposition, reports, corrections, and outcome-ready learning signals.
Live counters are active, but outcome-confirmed reliability claims still need more confirmed pairs.
Distributed intelligence, not a single deployment.
VetIOS scales as a distributed intelligence network.
Each cluster can ingest, infer, simulate, and report locally while contributing to the shared system graph.
An operator surface built like a system console.
The interface is designed as a control plane: visible inputs, observable execution, and direct feedback from outcomes and simulation.
{
"model": { "name": "VetIOS Diagnostics", "version": "latest" },
"input": {
"input_signature": {
"species": "canine",
"symptoms": ["vomiting", "lethargy"],
"metadata": {
"labs": { "wbc": 4.1, "pcv": 29 },
"hydration": "low"
}
}
}
}Console metrics above are static examples for the landing preview, not real-time production numbers.
API-first, typed, and observable.
The platform exposes clear runtime contracts, structured payloads, and direct operational signals for every major loop stage.
Examples below match authenticated /api/* routes (session cookies or platform scopes). External integrations typically use api.vetios.tech/v1— see the OpenAPI specification or developer hub.
curl -X POST https://api.vetios.tech/api/inference \ -H "Authorization: Bearer $VETIOS_API_KEY" \ -H "Content-Type: application/json" \ -d @case.input.json
{
"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": [ … ] },
"cire": { "phi_hat": 0.71, "cps": 0.12, "safety_state": "nominal" },
"meta": { "tenant_id": "…", "request_id": "…" },
"error": null
}curl -X POST https://api.vetios.tech/api/outcome \ -H "Authorization: Bearer $VETIOS_API_KEY" \ -H "Content-Type: application/json" \ -d @outcome.json
{
"inference_event_id": "11111111-1111-4111-8111-111111111111",
"outcome": {
"type": "confirmed_diagnosis",
"payload": {
"label": "canine_parvovirus",
"confidence": 0.98
},
"timestamp": "2026-04-14T12:00:00.000Z"
}
}{
"outcome_event_id": "evt_2841…",
"clinical_case_id": "case_4XK3…",
"linked_inference_event_id": "11111111-1111-4111-8111-111111111111",
"request_id": "…"
}curl -X POST https://api.vetios.tech/api/simulate \ -H "Authorization: Bearer $VETIOS_API_KEY" \ -H "Content-Type: application/json" \ -d @simulation.json
{
"steps": 10,
"mode": "adaptive",
"base_case": {
"species": "canine",
"symptoms": ["vomiting", "lethargy"],
"metadata": { "wbc": 4.1, "pcv": 29 }
},
"inference": { "model": "VetIOS Diagnostics", "model_version": "latest" }
}{
"simulation_event_id": "sim_901A…",
"clinical_case_id": "…",
"stability_report": { … },
"request_id": "…"
}Throughput and retention figures are illustrative marketing examples, not live telemetry.
Built from production primitives.
The stack is arranged as interoperable modules, not decorative logo placement.
Public surface and operator console delivery
Typed application contracts across runtime boundaries
Auth, session state, persistence, and event adjacency
Versioned clinical rules with outcome calibration and optional provider augmentation
Outcome, simulation, and observability fanout
Fast edge delivery for interface and control plane surfaces
Build on the layer competitors cannot copy quickly.
VetIOS is building the provenance-scored, outcome-confirmed clinical evidence layer underneath veterinary AI, AMR intelligence, and federated model promotion.
FOR DIRECT ASSISTANCE: partnerships@vetios.tech