Training
Reasoning-rich examples
Clinician-authored query, context, reasoning, answer, citation, and safety tuples for supervised learning or prompt improvement.
Reasoning trace
Hypothesis map
Patient context
Input bundle
Clinical Reasoning Sandbox for Health AI
Encoding physician logic behind clinical decision-makingacross the entire spectrum of patient care.
How It Works
Make clinical reasoningpaths inspectable.
We focus on clinical reasoning: the chain of small decisions that determines what to ask, what to rule out, what to weigh next, and when to escalate.
Clinical reasoning board
Physician-authored environment
A physician prepares the patient query, medical history, and records, plus safety boundaries and plausible distractors.
Physician input
What the doctor sees
What information is available before the encounter?
Medical history
Records
Labs
Goals
Reasoning loop
How judgment changes
1
Author
2
Read
3
Ask
4
Update
5
Iterate
6
Review
Case materials
Patient query, history, records, labs
Boundary
No reasoning outside authored context
False leads
Plausible distractors included
Trace output
Current step
Case materials
Medical history, records, labs, and goals included
Review note
A step-level summary for clinical review.
The environment is authored first. A second physician reasons through it, iterates as context changes, and leaves a reviewed trace behind.
Applications
Physician logic becomes product infrastructure.
The same physician reasoning layer can guide product workflows, power context-aware experiences, and generate the data needed to train and evaluate.
Live data layer
Context ingest
Expert trace
Eval output
Reasoning layer
The same trace can train, evaluate, personalize, and guide product behavior.
01
Health workflows
Review, labs, visit prep, fitness, nutrition, sleep, and guided task surfaces.
Agents
02
Explore health topics
Source hierarchy, personalization, practical guidance, and safer follow-up paths.
Queries
03
Labs and history
Biomarkers, vitals, workouts, appointments, prior tasks, and trend context.
Context
04
Records and files
PDFs, wearables, medical records, and patient background mapped into usable inputs.
Inputs
Evaluation
Gold-standard evals
Physician-written ideal responses and quality criteria that make quality changes visible across product iterations.
Alignment
Preference signal
Clinician comparisons that explain why one reasoning path, follow-up question, or answer is safer and more useful.
Product logic
Canonical question trees
Specialty-specific follow-up logic. The right next question for a cardiology concern is different from a dermatology concern.
Safety
Safety review
Targeted review for missed escalation, premature reassurance, unsupported claims, and context gaps.
Personalization
Context-aware training data
Reasoning that adapts to patient history, medications, comorbidities, goals, and connected health data.
Partners
A network shaped by medicine, research, and technical and financial execution.
High-quality experts
Physicians matched to the exact specialty each query demands.
Fast expert deployment
Staff physician review and deliver trusted traces in days, not months.
Time to qualified expert
Match the right specialist, then turn judgment into trusted traces.
FAQ
Questions worth answering early.
One physician authors the clinical environment: the patient query, medical history, records, red herrings, and safety boundaries. A second physician works through that environment and records the reasoning trace.
Contact
Build clinical reasoning sandboxes
for health AI.
Tell us what capability, specialty, or evaluation target you are building toward. We will follow up with the right clinical and technical path.