REGULATED LIFE SCIENCES
Decisions you can defend.
AI is fast but can't prove its work. In regulated life sciences, that's disqualifying. CoReason constrains AI as it works, so every output follows the standards your field already trusts and every step is recorded as it happens. Evidence you can hand to a regulator, an auditor, or a payer.
Grounding
Where a standard exists, the model doesn't get a vote. A code is retrieved from MedDRA, RxNorm, SNOMED, OMOP — or it does not appear.
The record
Written as the work happens, not reconstructed after. What an auditor reads is the actual path, including where a human intervened.
Re-runs
Every output versioned and re-runnable. Run it again in a year: same answer. A divergence is a finding we surface, not a surprise you meet in an inspection.
Sign-off
Judgment calls are held for a named human, with the evidence assembled. Nothing ships unsigned.
Watch it work.
Six stages of an asset's life, one shape of work: something unstructured goes in, every step is grounded in the method your field already trusts, and every run ends the same way: someone else re-runs it, and it matches.
WHY WE ARE DIFFERENT
AI shouldn’t guess where a standard already exists.
A general-purpose model doesn’t reason badly. It reasons where your field already settled the question, and can’t tell you how it got there. Three things follow.
Where a standard exists, the model doesn’t get a vote.
Your field spent decades building what a model is tempted to improvise: vocabularies, causality frameworks, pricing formulas, registries. A code is retrieved or it does not appear. A number is computed or it is not reported. Reasoning happens around those fixed points, never through them.
USE THE METHOD, DON’T APPROXIMATE IT
01
The record is written as the work happens.
An explanation written after the fact is a story about a decision. Each step is captured as it is taken, including where a human intervened, and why. What an auditor reads is the actual path. Where a claim has no source, the record says so.
THE TRAIL IS THE ARTIFACT
02
Run it again in a year. Same answer.
Reproducibility is the basis of defending a result to someone who wasn't in the room. Outputs are versioned and re-runnable. A re-run that diverges is a finding we surface, not a surprise you meet in an inspection.
THE TRAIL IS THE ARTIFACT
03
Underneath all three: everything we produce is reproducible and defensible to a third party. Data you can trace. Cohorts anyone can re-run. Packages a reviewer can sign. Same verb, different object. That is the whole company.
WORKING WITH US
Start small. Keep what we find.
We don’t ask you to commit to a program before either side has evidence. Every engagement follows the same path.
It starts with a working session. Thirty minutes, no deck. Bring the actual problem: the codelist nobody trusts, the cohort that won’t reproduce. We walk through it and show you the trail it leaves.
Then a two-week discovery sprint. Fixed fee, credited in full against the first delivery engagement awarded within ninety days. Week one, working sessions with your data and clinical teams: we map what you have, what you’re being asked to produce, and where the two don’t meet. Week two, we build one narrow worked example end to end on your own material and write it up.
You keep the output either way. A written assessment of your current estate, a worked example you can show internally, and a costed plan with a named team and a delivery date. If the assessment is useful and the plan is credible, that’s the basis for a larger conversation. If not, your exposure was two weeks.
TEAM
We've been the ones checking.
The data standards 300+ institutions run on. A launch carried to $6.5B. FDA breakthrough designations. This team has spent its careers on the reviewing side of evidence.
Gowtham Rao, MD, PhD
CEO / FOUNDER
Board Certified Physician licensed in NY, PA, WI, SC. PhD in Epidemiology and Biostatistics. Led development of observational health data systems adopted by 300+ institutions globally through OHDSI. Senior Director at Johnson & Johnson. Life Sciences Consultant at EPAM Systems. Former Chief Medical Informatics Officer at BlueCross BlueShield. VA Research Physician. 15+ years building the infrastructure for how the pharmaceutical industry generates and evaluates clinical evidence. The reasoning frameworks and data models in CoReason are deeply informed by that work.
Troy Sarich, PhD
Senior Strategic Advisor
20+ years at Johnson & Johnson. Former SVP & Chief Commercial Data Science Officer. Led XARELTO® from development through $6.5B in global sales. Co-founded the J&J AI Council.
Trilok Parekh, PhD
Senior Strategic Advisor
25+ years at J&J. Oncology CDT Lead. FDA Breakthrough Therapy Designations. Biomarkers & Real-World Evidence.
Amit Parikh, Esq
Strategic Advisor
IP & Technology. AI governance, patent strategy, equity structuring.
Asha Mahesh
Data Officer
Former JNJ Exec, Data Platforms & Privacy. Enterprise data architecture, governance, and compliance.
Ammar Shallal
Founding Investor
Operator and early-stage investor. 5+ ventures built across technology and services.
David Youmans, MD
Clinical Advisor
Healthcare executive with 25+ years of leadership in the clinical research organization (CRO) industry. Former Chair of Radiology at Penn Medicine Princeton Health, bringing combined expertise across clinical trials and health system operations.
WHERE OUR TEAM HAS WORKED
Johnson & Johnson · Roche · OHDSI · Bristol Myers Squibb · Penn Medicine · Accenture · BlueCross BlueShield