About Genomarker
Science is about to be produced faster than it can be trusted.
AI agents and automated pipelines are making computational analysis abundant. Evidence has not kept pace: methods go unchecked, provenance is incomplete, and results expire with their software. Genomarker exists to close that gap — a verification layer between scientific computation and scientific claims, so that any result carrying our statement can be inspected, verified, and independently replayed by people who never met its authors.
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 must be verifiable offline, without the Genomarker application, a decade after the software that produced it is gone.
Block bad science early
The cheapest failure is the one caught before execution. Methodological rules run against the spec, 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 apply the assurance model to published science we had no role in preparing, and publish what we find — including what breaks.
“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 DEMONSTRATION · GENOMARKER
Team
Built by people who have lived the reproducibility problem.
Salar Sayyad
Founder & CEO
Architect of the entire Genomarker platform — the assurance kernel, evidence format, and replay system — built solo, full-time since Feb 2026. 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 with 25+ years in machine learning and predictive modeling, focused on biomarker discovery and precision medicine. Inventor of the Real Time Learning Machine and author of the widely used An Introduction to Data Science. Advises on statistical methodology and the assurance rule library.
Dr. Mark Hiatt, MD, MBA, MS
Scientific advisor · clinical & regulatory
Stanford-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.