Model + agent gateway
Set approved models, tools, data boundaries, and hard constraints before intelligence reaches a clinical workflow.
Cognition Clinical · Hospital systems
The governed runtime between AI and care delivery.
A clinical control plane across models, agents, applications, evidence, and care teams. Every consequential output stays grounded, reviewable, and reconstructable.
Hospital AI stackGoverned
IntelligenceFrontier models · internal models · clinical agents
ApplicationsScribe · decision support · inbox · research workflows
Governed runtimeCognition Clinical · context · provenance · constraints · review
Care deliveryClinicians · operations · compliance · informatics
The runtime in practice
Synthetic clinical demonstrationGovernance appears inside the work, not after it.
Cognition brings the required context, unresolved questions, evidence review, and authorized approval into the same interface where the clinical output is produced.
Governed clinical workflowContext → evidence → review
One control plane
Govern the system, not just the model.
Clinical risk is created by the interaction between models, prompts, tools, retrieved context, application logic, and human review. Cognition makes that full chain explicit and inspectable.
Application governance
Turn each production prompt and workflow into a versioned clinical contract with required context and verification rules.
Care-team approvals
Route consequential outputs to the right clinician, preserve corrections, and make release decisions reconstructable.
From prompt to clinical receipt
Every output becomes a reconstructable decision.
The runtime does not merely log an answer. It preserves what the system needed to know, what evidence it used, where uncertainty remained, who approved the output, and what changed afterward.
What must be known
Declare the patient state, policy, protocol, and workflow context required before the model answers.
What supports the output
Attach source material and distinguish retrieved fact from inference or missing information.
Who owns the decision
Send the output to the authorized care-team role with unresolved questions visible.
What must survive
Preserve the version, reasoning, approval, corrections, and conditions for retesting.
Deliberate deployment
Start with one governed workflow.
One model→One application→One care team
Establish the context contract, run synthetic and de-identified failure cases, measure review burden and critical omissions, then expand only when governance evidence supports it.
Designed for the full institution
A shared operating record for clinical AI.
Clinical informatics sees workflow behavior. Compliance sees approved boundaries and exceptions. Care teams see the context needed to review. Engineering sees versioned contracts and failure modes. Everyone works from the same reconstructable record.
StakeholderWhat becomes visibleWhat becomes governable
Care teamsEvidence, missing context, open questionsReview, correction, approval, handoff
Clinical informaticsWorkflow behavior and failure patternsRequired context and release criteria
Compliance + securityData boundaries, policy, lineage, exceptionsPermitted tools, roles, retention, escalation
EngineeringPrompt, model, retrieval, and application versionsContracts, evals, rollback, retesting
A governed starting point
Put one consequential workflow under control.
Bring one model, one application, and one care team. We will map the context, constraints, review path, failure cases, and evidence required before any broader deployment.
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