Coda Health

First-principles reasoning data for health AI.

We capture the chain of hypotheses, questions, evidence, and revisions that clinicians use to move from uncertainty to action.

Clinician-led

Structured traces

Specialty-aware

Our Mission

Clinical reasoning turns uncertainty into action.

Coda externalizes the clinician's internal reasoning and turns it into structured traces that health AI can train on, evaluate against, and improve.

How It Works

The reasoning path, made inspectable.

Coda captures not just the final answer, but the chain of clinical judgment that produced it.

Clinical note

Eval rubric

Evidence pack

Trace record

Reasoning trace

Every answer carries a trace.

Clinicians revise as new context appears. Coda captures each turn as structured data.

Input

Hypothesis

Question

Evidence

Update

Trace

Input

Patient context, history, labs, symptoms, goals

Hypothesis

What is likely, what is dangerous, what is missing

Question

The next prompt, test, or discriminator

Evidence

New signal changes confidence and priority

Update

The differential narrows, shifts, or escalates

Trace

A structured record of the reasoning path

What This Enables

Reasoning 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.

Reasoning layer

The same trace can train, evaluate, personalize, and guide product behavior.

Health workflows

Review, labs, visit prep, fitness, nutrition, sleep, and guided task surfaces.

Agents

Explore health topics

Source hierarchy, personalization, practical guidance, and safer follow-up paths.

Queries

Labs and history

Biomarkers, vitals, workouts, appointments, prior tasks, and trend context.

Context

Records and files

PDFs, wearables, medical records, and patient background mapped into usable inputs.

Inputs

Training

Reasoning-rich examples

Clinician-authored query, context, reasoning, answer, citation, and safety tuples for supervised learning or prompt improvement.

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.

Team

Built by clinical and technical operators.

Coda combines physician-led quality with the operating discipline to turn expert judgment into reliable data.

Dev Patale

Co-founder

Technical background (MIT CS), spent the last few years in tech banking and software investing. Knows how to structure expert judgment into data products that technical buyers trust.

Coatue Management

Analyst · Software L/S · 2026

KKR

Private Equity Associate · Americas TMT · 2025 - 2026

Morgan Stanley

Investment Banking Analyst · Global Technology M&A · 2023 - 2025

MIT

S.B. Computer Science & Economics · 5.0 GPA · Phi Beta Kappa · 2019 - 2023

Stanford GSB

MBA · Deferred Admit

Roger Zou, MD, PhD

Co-founder

MD-PhD physician with direct access to a network of board-certified physicians across multiple specialties. Oversees physician recruitment, quality criteria, and clinical review.