AI is expanding into every vertical. The companies building that future will need structured, validated domain engines to reason against. GeneOps is that engine for human biology.
Genome sequencing costs have collapsed faster than Moore's Law — from billions to under $200 today, with sub-$100 within reach.1 Simultaneously, AI agents have reached clinical-grade performance on medical reasoning benchmarks. This intersection creates a new category.
Wellness platforms, fitness apps, supplement brands, and AI agents all want to personalize by genotype. None of them have the infrastructure to do it. The missing piece isn't consumer demand — it's the structured, validated intelligence engine that makes genomic personalization possible.
The tailwinds are structural. Falling costs, rising curiosity about personal biology, and a generational shift toward proactive self-optimization over reactive healthcare.
Every AI agent, wellness platform, and health product that wants to personalize by genotype needs the same thing: a structured, validated, continuously curated domain layer to build on. Without it, AI hallucinates and platforms guess. That substrate doesn't exist today. GeneOps builds it.
They confuse risk alleles with reference alleles. They miss gene-gene interactions. They cite retracted papers with confidence. No platform, agent, or product can build reliable genomic features on raw AI alone.
Every recommendation grounded in published research. Evidence-graded. dbSNP-verified. Schema-validated with PubMed citations. Partners plug in and ship genomic personalization on day one — without building domain expertise.
Within 3–5 years, most people's primary interface with digital services will be an AI agent, not an app. But the domains where agents can be trusted are vanishingly few.
Most AI agent startups are building in low-stakes domains — shopping, scheduling, travel. We picked the hardest, highest-trust domain on purpose. Biology is where the rigor is non-negotiable, where the credibility barrier is highest, and where a working substrate is hardest to replicate. That's not a handicap. It's the moat.
Two compounding vectors. One deploys AI agents into people's lives. The other builds the data infrastructure that all agents will need. Both compound with scale.
Each vertical deploys an agent that knows the user's genome, remembers their context, and evolves with them. Allela is your wellness advisor. Darwin is your dating counselor. A GP-facing vertical puts a genomics agent in the doctor's office. Each vertical is a relationship, not a dashboard. We are getting extremely good at building agents that people trust with intimate, consequential decisions about their bodies.
When every health app has an AI agent, and every agent needs structured biological data to reason safely, we are the pipe they connect to. MCP-native from day one. The knowledge base is machine-readable by design — built for agents, not just for humans. The next wave of health technology will be built on top of infrastructure like this.
Most genomics platforms were designed for a human to read a report. GeneOps was designed for an agent to reason against structured data. The dashboard, the API, and the MCP server all consume the same knowledge layer — the consumer product is a view, not the core.
Full data sovereignty. Pair with a local model — Llama, Mistral, DeepSeek — and genetic data never leaves the user's machine.
Cross-reference anything. Hand your agent a blood panel, a prescription, a research paper. It reads them alongside the user's full genetic profile.
The most powerful way to organize your life. Power users plug their own agent into GeneOps and let it weave their genome into everything else it does for them — meal planning, training, supplement choices, conversations with their doctor. As agents take over more of daily life, this becomes the default interface to every service. GeneOps is just at the bleeding edge.
We don't compete with AI labs — we build the domain layer they need. As models improve, as genetics research deepens, as consumer testing tech expands, the value of a deterministic, validated substrate compounds.
As AI expands into health, every major platform will need structured genomic intelligence to build on. This isn't something they can train into a general model — it requires deterministic, per-variant curation against published research. GeneOps becomes more valuable with every improvement in AI capability.
Gene-gene interaction maps, evidence-graded variant recommendations, genotype-specific action plans — compiled from thousands of papers into structured schemas. It can't be scraped, prompted, or generated because it doesn't exist in this form until we build it.
You can't prompt-engineer your way to safe genomic recommendations. The wrong call on a clinically actionable variant has real consequences. This requires deterministic validation — not probabilistic generation. That's why AI companies will integrate this, not build it.
Commercial genomics today is held by a handful of established players that made the correct architectural bets for the decade when sequencing was scarce, expensive, and clinical-only. Those bets are now structural commitments.
The 50M+ raw genotype files already in users' hands are a margin problem for a lab business — not an asset to build on.
A salaried geneticist team caps how fast the knowledge base can deepen — and what it costs to deepen it at all.
Hard to turn around in a quarter to serve a consumer-brand founder shipping in weeks, or an agent platform shipping in days.
Going agent-native on top of a presentation layer built for PDFs is a rebuild from the schema upwards — not a feature.
None of these are bad companies. They're competently run businesses that made sound bets for the era they were built in. The category isn't waiting for an incumbent to fix itself — it's waiting for a company built from scratch on the new economics. That's the opening.
The opening move and the endgame are not the same company. GeneOps enters where the existing players don't compete — and matures into the layer they end up depending on.
Online health personalities. AI-native consumer brands. The agent ecosystem. Partners who ship fast, own their brand, speak directly to audiences who already trust them, and have no in-house genetics team to retire. This corner sits outside the existing players' field of view — and it's where the next decade of consumer health is forming.
As GeneOps matures — more variants, deeper interactions, sharper agent integration, more verticals on the engine — the analysis and AI-tooling layer the incumbents run in-house stops being competitive with what GeneOps offers as a service. They keep their labs. The intelligence layer becomes ours. The platform stops being an alternative and starts being the substrate the industry runs on.
The largest pool of human genomic data on Earth already exists — exported, downloadable, sitting in 50M+ inboxes from 23andMe and AncestryDNA. For a company with a lab to feed, that pool is a competitor. For a company without one, it's the runway.
A lab-dependent business has to keep the kit funnel full to fund the lab. BYO users can be upsold to premium panels, but they remain a lower-margin segment — and the headline acquisition motion stays pointed at people who haven't been tested yet, not at the 50M+ who already have a file.
GeneOps offers white-label kits via partner labs where a partner wants them — and welcomes BYO files on equal footing because the platform's economics don't depend on the kit margin. A user uploads a file they already own and is in the product in minutes.
A structured knowledge base of genomic research, paired with a deterministic matching engine and an agent-native API layer. Partners — wellness platforms, AI agents, labs, fitness apps — integrate once and ship genomic personalization immediately. We run our own consumer vertical as proof that the infrastructure delivers real value.
Structured, evidence-graded, per-variant research. Continuously expanding. The substrate that AI reasons against.
Deterministic engine maps any genotype against the knowledge base. Exact and reproducible — same input, same output, every time.
API, MCP server, white-label dashboard, or embedded widget. Partners choose the integration surface. We deliver the intelligence.
Evidence-graded actions: supplements, nutrition, lifestyle, training, monitoring, avoidances — personalized to each user's genotype, not their demographic.
"Evidence-graded" is the marketing claim. This is the pipeline behind it. Each variant in the knowledge base passes through frontier-model research, multi-stage verification, and an independent audit cycle before it ships.
Frontier-model agents under human curation. Per-variant deep research, schema-validated, version-controlled.
dbSNP cross-check. GRCh38 plus-strand orientation. Any ambiguity halts the entry.
Frequency cross-checks against gnomAD, 1000 Genomes, and TOPMED. Discrepancies block the entry.
A separate driver re-fetches every PubMed citation, re-checks orientation, re-grades the evidence.
A batch ships only after three consecutive audit cycles produce zero findings. Anything less, it goes back.
This is what "deterministic" actually costs to build — and the reason a general-purpose model can't generate it on demand. The substrate becomes more valuable with every improvement in model capability, not less.
This is our own consumer vertical — running live on the same infrastructure we offer to partners. Immediate value from automated actions. Infinite depth through AI. A partner integrating our API or white-label product ships this experience without building any of it.
Your body converts folic acid at roughly 65% efficiency. The active form bypasses the bottleneck entirely.
Your muscle fibers are shifted toward aerobic performance. Marathon over CrossFit.
Multiple gene variants raise your cardiovascular risk marker. Easy to monitor, easy to manage.
Same engine, different face. Each vertical runs on the GeneOps intelligence infrastructure — knowledge base, matching engine, AI agent, payment, onboarding. Configuration, not development. These are all live today.
Knowledge base · Matching engine · Agent layer · MCP server · Data platform
These aren't reskins — they're completely different products with different feature sets, different user flows, different business models. A B2C wellness companion and a B2B roster management tool share a genomic intelligence layer, but almost nothing else. That's the power of the platform: we can go from concept to deployed product in days of planning and design — at a speed that lets each partner ship a category-leading product without rebuilding the underlying genomics work.
Full intelligence engine operational — knowledge base, matching engine, API, MCP server
13,135 personalized actions, 2701+ gene interactions across 41 health categories
13 verticals live on the platform today
AI agent + MCP server production-ready for partner and external agent integration
Knowledge base browsable free at geneops.ai/research — public proof of domain depth
Rapid vertical deployment across consumer, medical, and institutional markets
3,000+ variants — deeper rare variant coverage
Genotype-matched marketplace — supplements and products matched via API for partners and direct users
Phenotype feedback loop — self-reported outcomes across all partner channels refine recommendations
Original research — GWAS and other studies on our own genotype-phenotype dataset as it reaches research scale
Partner revenue is the core — every integration leverages the same knowledge engine at near-zero marginal cost.
Labs, wellness platforms, fitness apps, and supplement brands embed our intelligence into their products. Per-query pricing or volume licensing. Instant genomic expertise from integration.
Full branded experience running on our infrastructure. Partner owns the relationship, we power the intelligence. Revenue share or flat licensing.
As AI agents become the primary interface for health decisions, the platform that feeds them structured genomic data captures compounding value. MCP-native from day one.
Each vertical prices independently for its market. Consumer wellness, sports, dating, localized markets — every vertical generates subscription revenue on the same engine.
Supplements, vitamins, and products matched to each user's genotype. 10–25% commission. Available to partners as an embedded feature.
Every user interaction generates genotype-phenotype correlations. We use it in-house to sharpen recommendations and deepen our gene-interaction maps — and as the dataset grows, to underwrite original research.
A conventional commercial genomics company carries a research team of geneticists, a lab, an enterprise sales cycle, and a roadmap dictated by the speed of those three things. GeneOps runs on a different operating model — and every layer you see in production was produced under it.
Per-variant deep research, multi-stage verification, an independent audit cycle. Production at a depth that a salaried research team would take years to match — and at a velocity a salaried team cannot match at all.
Matching engine, REST API, MCP server, the consumer flagship at geneops.ai, and every partner vertical — built end-to-end with frontier-model agents in the loop. One technical founder producing the output of a team.
Commercial colleagues working heavily with agents from day one. Tech builds bespoke tooling for the commercial workflow; commercial feeds tech the constraints worth solving next. The leverage compounds across functions.
Software-leveraged in place of headcount-leveraged — end to end. This is not a temporary stage to outgrow; it's the operating model the company is built on, and the source of the speed advantage that every partner integration inherits.
Johan takes products to market at scale. He's done it in telecom, he's done it in media, he knows how to build distribution. Martin builds at AI-native speed. One person produced what would traditionally require a sizable team. Previous joint ventures between the two, including an ambitious media project with institutional investment. Deep mutual trust, proven working relationship.
GeneOps Inc. is the Delaware C-Corp parent. GeneOps AB is the Swedish operating subsidiary, where the team and the build are. Production infrastructure runs in the EU.
Structurally legible to US investors and acquirers. Operationally positioned to sell into the European market on day one. Two readings, one structure.