A raw genotype file contains hundreds of thousands of variant calls. Converting that into a coherent, evidence-graded, personalized health picture requires a curated research substrate, a structured matching engine, and a delivery mechanism designed for the use case. Most people who get a DNA test receive neither.
Consumer genomics generated an enormous amount of raw data and almost no useful intelligence downstream of it. The products that do exist are mostly static reports, shallow in science, and silent once the report is delivered. The re-engagement rate is close to zero.
Without a rigorously curated research substrate, genomic outputs are either too cautious to be useful or dangerously overconfident. Most products choose one of those failure modes. GeneOps is built around neither — evidence grading is structural, not a feature.
Three curves converge today: sequencing cost (near-zero), AI capability (clinical-grade reasoning), and consumer health sophistication (rapidly growing). The infrastructure layer between these trends and personalized health products doesn't exist yet. That's the company.
Not a pitch deck infrastructure claim. A live, deployed platform that partners can integrate today.
1477+ SNPs across 30 health domains. Evidence-graded by replication, effect size, and study quality. Every finding linked to at least one PubMed-citable source. Built through 6,520+ papers reviewed and continuously expanding as new research is curated.
A production matching engine that ingests standard genotype files and traverses the research substrate to produce personalized outputs. 9,017+ genotype-specific actions and 1682+ gene–gene interactions surfaced per genome. Reproducible, auditable, stable.
REST API, MCP server, white-label dashboard, and embedded components — four distinct integration surfaces serving different partner architectures and product requirements. Each surface exposes the same underlying intelligence; partners choose the appropriate depth of integration.
The full research database is publicly accessible at geneops.ai/research — every variant, every citation, every confidence grade. This transparency is intentional: we build on evidence, and the evidence should be visible.
Every output in the GeneOps platform carries an explicit confidence grade. This isn't a feature — it's an architectural requirement. Outputs that can't be graded don't enter the knowledge base. The system is designed to be honest about uncertainty, not to paper over it with confident-sounding language.
GeneOps is deliberately designed as infrastructure. We don't compete with partners — we power them. This means the white-label surface is as important as the API, the science is as important as the UI, and the business model is built around partner success rather than direct consumer relationships.
Genomics research advances faster than most fields. The GeneOps knowledge base is designed to improve continuously — not at release milestones, not at quarterly review cycles, but as new evidence is curated and validated. Partners who integrate GeneOps today have more powerful intelligence in a year without changing their integration.
The consumer genomics market is estimated at $13B by 2034, growing at ~25% CAGR. The intelligence layer between raw genomic data and personalized health action — the layer GeneOps occupies — is in early innings. The partners who build on this infrastructure now are positioning for a category that will be significantly larger in five years than it is today.
Whether you're a potential partner, an investor, or someone with a perspective on the space — we're interested in the conversation.