Partners and Investors
Clinical Corvus is a clinical workflow copilot for rounds, handoffs, and evidence-aware plan drafting. The current focus is narrow and concrete: helping acute care teams reduce cognitive load and improve consistency in workflows that already exist.
Company definition
EN: Clinical Corvus is a privacy-first clinical workflow copilot that synthesizes fragmented patient data, supports rounds and handoffs, and helps clinicians generate evidence-aware plans in an auditable environment.
PT-BR: Clinical Corvus é um copiloto de workflow clínico privacy-first que sintetiza dados fragmentados do paciente, apoia rounds e handoffs, e ajuda clínicos a gerar planos orientados por evidência dentro de um ambiente auditável.
Current stage
The system is in limited beta with an explicitly defined product boundary. It is not an autonomous diagnosis platform or an automated treatment planning system.
Supported: clinical question with context, differential synthesis, plan drafts under clinician review, I-PASS, evidence retrieval with explicit citation
Warned: questions with insufficient evidence — the system declares uncertainty
Blocked: requests requiring autonomous diagnosis, automated treatment planning, unrestricted advice
Wedge and buyer
The adoption wedge is simple: the system starts with clipboard and documents, without needing deep EHR integration. Teams that already use spreadsheets, messaging apps, and copy/paste to operate can start on day one.
The primary buyer is the clinical team (physicians, residents) who feel the cost of fragmented context daily. The investment case is productivity and handoff consistency, not automation.
Pilot structure
A valid pilot answers:
- Does the team build the patient picture and draft the plan faster, with less rework?
- Do I-PASS drafts stay consistent, editable, with fewer omissions?
- When the team asks for evidence, are sources relevant and easy to check?
- Do clinicians review and edit drafts, not accept on autopilot?
- Does the system fit the privacy, governance, and documentation constraints of the target setting?
Why the architecture matters
The architecture is not an engineering detail. It is what enables diligence-level claims:
- PHI stays inside the backend by default
- The system exposes explicit uncertainty when evidence is weak
- Drafts are clinician-reviewed by design, not by disclaimer
- The system knows what it cannot answer reliably
The multi-path architecture (CDA, CRA, bounded orchestration, PHI egress gate) exists because this boundary is specific, not because it is a generic platform.
What we are not claiming
- We are not claiming autonomous diagnosis or automatic treatment
- We are not claiming complete clinical validation as a medical device
- We are not claiming that every AI flow traverses a single pipeline
- We are not claiming broad "safe results" or "validated outputs"
Current proof targets
- Benchmark execution through canonical task pipeline
- Dashboard replay review for alignment and uncertainty handling
- Workflow checks for state continuity
- Alpha/pilot operational signals
The proof roadmap is measuring workflow impact (round time, handoff consistency, evidence usage) in controlled pilots.