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.
Building a verifiable clinical dataset
VetIOS now reports the evidence it can verify: case intake, confirmed labels, CIRE validation coverage, and workflow signals from the connected platform. When public dataset access is not configured, this section says so plainly.
Awaiting a configured public tenant before reporting dataset scale.
0 cases are currently marked learning-ready.
Awaiting outcome-linked inference pairs before reliability claims are evidence-grade.
2 PIMS packs and 5 passive event types are defined.
0 reviewable CDS drafts and 0 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.
Awaiting outcome-linked inference pairs before reliability claims are evidence-grade.
Append-only review events can capture specialist disposition, reports, corrections, and outcome-ready learning signals.
Public evidence tenant is not configured, so the page reports architecture only.
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
Custom model inference with OpenAI fallback only
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: johnbruce12g@gmail.com