About Genomarker
Every consequential claim should come with evidence anyone can check.
AI agents and automated pipelines are making computational analysis abundant. Independent checking has not kept pace: methods go unexamined, provenance is incomplete, and results expire with their software. Genomarker exists to close that gap — independent verification that lets anyone rely on a result without trusting who, or what, produced it.
Principles
Six commitments the product is built around.
Assurance over automation
The bottleneck of AI-accelerated science is not generating analyses — it is knowing which results deserve trust. We build the layer that decides.
Precise claims only
Genomarker never says “certified correct.” It says exactly what was verified, under which versioned rules — and states plainly what it does not establish.
Evidence outlives infrastructure
Every claim should stay verifiable without Genomarker, a decade after the software that produced it is gone.
Block bad science early
The cheapest failure is the one caught before execution. Our checks run against the analysis request, not the result.
Agents are clients, not exceptions
Human, pipeline, or AI agent — the same rules, the same evidence, the same replay. The actor is recorded; the standard never moves.
Audit ourselves in public
We applied our checks to published science we had no part in and reported what we found — including 24 problems in our own platform.
“An independent researcher in 2036 — without the original scientist, the original AI agent, or the original application — can verify, reconstruct, and replay a computation we sealed in 2026. That is the standard everything else is built toward.”
THE NORTH STAR · GENOMARKER
Team
Who's building it.
Salar Sayyad
Founder & CEO
Built the Genomarker platform end to end, solo and full-time since Feb 2026 — five releases in under six months and a nine-study public audit run on production. 10+ years as an AI engineer, data scientist and product manager; BSc Computer Science, University of Toronto.
Dr. Saed Sayad
Scientific advisor · AI & bioinformatics
PhD in biochemistry & bioinformatics; adjunct professor at the University of Toronto and former professor of data science at Rutgers University, with 25+ years in machine learning and predictive modeling, focused on biomarker discovery and precision medicine. Inventor of the Real Time Learning Machine and creator of the widely used online text An Introduction to Data Science. Advises on statistical methodology and the rule library.
Dr. Mark Hiatt, MD, MBA, MS
Scientific advisor · clinical & regulatory
Stanford fellowship-trained physician executive in precision medicine — former VP of Medical Affairs at Guardant Health, SVP of Market Access & Strategy at BostonGene, and CMO at RadSite; 75+ articles and book chapters, 150+ conference presentations. Advises on clinical, regulatory and market-access context for evidence standards.
Working on the same problem?
We talk to research teams, platform builders, methodologists, and the programs that back early scientific infrastructure.