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The Problem

The Platform

How We Build

Team

The Problem

The Platform

How We Build

Team

The Problem

The Platform

How We Build

Team

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THE PROBLEM

The question decays before the answer arrives.

An asset lead needs data. Maybe it's to support a claim. Maybe it's to protect a competitive moat. Maybe it's a go/no-go decision on a $200M investment. There are a thousand versions of this question, and they all start the same way: someone needs an answer, and they need it grounded in evidence.

What happens today

Week 0

The asset lead asks the question. It's urgent. The competitive landscape just shifted, or a payer raised a concern, or a clinical trial readout changed the calculus. The question goes to one team, sometimes two or three.

Week 1 - 2

Each team interprets the question through their own lens. They access different data sources. They use different frameworks. An epidemiologist hears "target population" and thinks about incidence rates in claims databases. A commercial lead hears "target population" and thinks about addressable market share. They're working on different problems without knowing it — because ideas and concepts are personal, and they're only shared approximately through language.

Week 3 - 4

The answers come back. They're imperfect — not because the teams are bad, but because they answered slightly different versions of the question. Worse, the question itself has decayed: the competitive landscape shifted again, or the regulatory context changed, or a new publication landed. The asset lead now has stale answers to an ambiguous question, and the cycle starts over.

The meaning problem

This isn't a coordination failure. It's the nature of how humans communicate across domains. When a medical affairs scientist says "value," she means clinical efficacy data that supports a label claim. When a payer economist says "value," he means cost-effectiveness relative to the standard of care. When a commercial lead says "value," she means the message that moves market share. Same word, three different problems — and no mechanism to surface the gap until the answers come back misaligned.

Knowledge doesn't hold still

A system that takes two to four weeks to return an answer is structurally incapable of keeping pace with the environment it's supposed to inform. Biomedical knowledge constantly changes — concepts update, relationships shift, clinical guidelines are revised. A competitor files new trial results. A payer agency updates guidance. The landscape you planned against six months ago no longer exists.

WHAT'S AT STAKE

The best drug launches don't just develop a therapy — they engineer the conditions for its adoption. That means aligning clinical evidence, regulatory strategy, payer economics, and population health before the drug reaches market. It requires shared precision about what each domain means by "target population," "screening criteria," and "market readiness" — and it requires that precision to hold across functions, in real time, as the landscape shifts. When it works, it's transformative. When it doesn't, you get a scientifically excellent drug that misses its commercial window. Most companies don't have a system for this kind of cross-functional reasoning. They have talented people holding it in their heads.

~66%

of launches underperform pre-launch expectations

— McKinsey

~50%

of Phase 3 clinical trials fail

— Parexel

$2.6B

average cost to bring one drug to market

— Tufts CSDD

HOW WE BUILD

AI's pattern recognition is always approximate, biased, and unreliable.

This isn't a criticism. It's the nature of what pattern recognition does. Data is always biased — after all, it's just samples. Training data selection is biased. Biomedical ontologies are constantly changing. Clinical guidelines are conventional approximations. These are well-understood problems in information science, and they have been for fifty years.

Most AI companies in life sciences are ignoring this. They build systems that produce confident outputs and treat them as reliable. We engineer around the opposite assumption: every AI output is approximate until verified. That produces systems that people can actually trust for high-stakes decisions.

Human judgment is not optional

In life sciences, the human expert isn't a nice-to-have in the loop — they're the one who bears professional and legal responsibility for the decision. They interpret data through experience, weigh trade-offs subjectively, and take accountability for outcomes. Medical ethics literature is clear on this: you can't automate that away, and you shouldn't try. We build the infrastructure that makes their judgment better-informed, faster, and auditable — not the infrastructure that replaces it.

Verification runs alongside generation

When the platform processes data, it doesn't just summarize — it writes deterministic code to verify its own outputs. If it claims two drug names refer to the same compound, it produces the matching logic as executable rules, not a probabilistic guess. The system reasons and verifies simultaneously. This dual architecture — description paired with validation — is what makes outputs auditable rather than approximate. It's the same structural insight behind the first clinical expert systems, rebuilt for modern AI.

Meaning standardization, not just search

Anyone can connect a language model to a search API. The harder problem is semantic: when three teams ask the same question using different terminology, the system must recognize they're asking the same thing. When they ask different questions using the same terminology, the system must surface the ambiguity. This is what the question-sharpening layer does — it resolves meaning before the agents begin their work, so the telephone game never starts.

Built on open standards the industry already trusts

Our CEO helped build the observational health data systems used by 300+ institutions globally through OHDSI. The platform extends the OMOP Common Data Model and ATLAS tooling — a decade of open, collaborative infrastructure for standardizing how health data is queried and analyzed. We didn't invent a proprietary standard. We built AI reasoning on top of the one the industry already uses.

The challenge of building trustworthy AI for medical decision-making traces back to the first expert consultation systems of the 1970s — systems that combined descriptive models of what we observe with normative rules for how to reason about it. Fifty years later, the core insight remains: reproducible decision-making requires that inference and verification run together, not sequentially. CoReason is built on this foundation.

THE PLATFORM

Sharpen the question. Swarm the answer.

You bring a question. The platform first makes sure you and the system mean the same thing — standardizing terminology, disambiguating concepts, structuring the question so that ten different people asking it ten different ways would produce the same query. Then teams of AI agents go to work in parallel: searching, cross-referencing, verifying, and synthesizing across public scientific, regulatory, and commercial sources. You see the reasoning happen. You guide it. The output is traceable, auditable, and ready in days or hours — not weeks.

01 SHARPEN

Align on what you're actually asking

The biggest source of wasted effort in evidence generation is ambiguity at the start. The platform takes your natural language question and decomposes it into structured clinical and commercial parameters — standardizing terminology so that "response rate" resolves to the same definition whether a clinician or a commercial analyst is asking. If you and the system aren't aligned on the question, no amount of searching will produce the right answer. This step eliminates the telephone game before it starts.

02 SWARM

Parallel agents, not serial handoffs

What used to require three teams working in serial over four weeks, the platform runs in parallel. Dozens of AI agents search PubMed, ClinicalTrials.gov, regulatory filings, HTA decision databases, and earnings transcripts simultaneously. They don't just retrieve results — they follow leads iteratively, verify claims against sources, and generate follow-up hypotheses at each layer. When one agent finds a competitor's dropped trial arm, another is already checking the regulatory implications while a third is modeling the payer impact.

03 PROVE

Every claim traceable to its source

The output is a structured, templated report — consistent format every time, so a reviewer who's seen one report can navigate any other in five minutes. Every data point links back to its source. Every exclusion is documented with rationale. Every inference is explainable. Underneath the report is the full data extraction: every source reviewed, every number traced, downloadable as a spreadsheet. If the system says 25 studies met inclusion criteria, you can see each one. If it says a competitor's Phase 3 endpoint changed between filings, you can pull both documents. The report is the summary. The extraction is the proof. Explainable to your team, auditable by anyone.

WHAT THE ALTERNATIVE LOOKS LIKE

With the old process, you got one answer — shaped by whichever team's assumptions happened to dominate. With CoReason, you get a map: where does the payer's view of your asset agree with the regulator's, and where does it conflict? What assumptions is your commercial forecast relying on that the medical affairs evidence doesn't yet support? These are the questions that determine whether a $200M investment pays off — and they're the questions that the telephone game structurally cannot answer.

THE TEAM

CoReason is based in New Jersey — not Silicon Valley. Our team comes from the pharmaceutical industry, not from tech companies looking for a vertical. We've run evidence generation programs, built data infrastructure for global health systems, led commercial strategy for multi-billion dollar drugs, and navigated the regulatory and payer landscapes firsthand. We're building the tool we wished we'd had.

Built in life science country, by the people who've done this work.

Our Team

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

Legal 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.

Name a drug and indication. Let us do the work.

We'll sharpen the question, swarm the data, and walk you through the synthesized intelligence — on something you actually care about. No slides. No pitch.

30 minutes. If the output isn't useful, you've lost half an hour. If it is, you've found a blind spot.