Describe your patient. Get ranked diagnoses in seconds.
VetIOS turns patient signs, history, and test results into clear possible diagnoses and recommended next tests.
The clinical view stays simple. The full console is still there.
One runtime. Five compounding stages.
VetIOS operates as a compounding intelligence loop, not a static model.
Structured signals enter the platform with typed context, lineage, and policy state.
Models resolve ranked clinical hypotheses with confidence bands and runtime traces.
Resolved cases stream back as supervisory signals with auditable attribution.
Counterfactual traffic is replayed before changes move into production control paths.
The system compounds into a stronger shared decision layer with every completed loop.
Platform modules for the entire clinical loop
Each layer is designed as infrastructure: typed inputs, observable execution, and system-level feedback.
Inference Engine
Clinical inputs are normalized, routed, and scored through a deterministic inference runtime with operator-visible confidence signals.
Outcome Learning
Closed cases become supervision events that refine priors, evaluation baselines, and future decision quality.
Simulation Layer
New models and policy changes are pressure-tested against synthetic and replayed case traffic before rollout.
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.
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.
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.
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.
{
"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
}{
"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": "…"
}{
"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 intelligence, not isolated decisions.
VetIOS is building the infrastructure layer for veterinary intelligence systems.
FOR DIRECT ASSISTANCE: johnbruce12g@gmail.com