The GeneOps platform is structured as three distinct but interconnected layers — each one independently strong, together forming a complete genomic intelligence system.
1477+ SNPs across 30 health domains. Evidence-graded, dbSNP-verified, PubMed-cited. 6,520+ peer-reviewed papers underpinning 1682+ gene–gene interactions. Continuously updated as new research is curated. This is the foundation that determines the quality of everything above it.
A deterministic analysis engine that ingests standard genotype files (23andMe, AncestryDNA, raw VCF) and traverses the knowledge substrate to produce a structured, personalized output. The engine is deterministic — identical inputs always produce identical outputs. No probabilistic inference, no hallucination risk in the analysis layer.
Four integration surfaces: REST API for backend integration, MCP server for AI agent use, white-label dashboard for complete product deployment, and embedded components for drop-in UI. Partners choose the integration point that fits their architecture — all surfaces expose the same underlying intelligence.
A structured JSON API for backend integration. Submit a genotype file, receive a complete genomic intelligence payload — variant matches, confidence-graded findings, personalized actions, and gene–gene interactions. Full documentation, sandbox environment, and versioned endpoints. The foundation for custom product experiences built on GeneOps intelligence.
A Model Context Protocol server that makes the GeneOps knowledge base queryable by AI agents in real time. Any agent or application using the MCP standard can plug in and receive structured, evidence-graded genomic data in response to natural language queries — with citation metadata intact. The integration point for conversational and agentic genomic products.
A complete, brandable genomic health portal deployable on your domain. Covers the full user journey from file upload to domain exploration to conversational AI interaction — customizable with your branding, typography, and color system. The fastest path from partnership to live product: days, not months.
Pre-built, themeable UI components that drop into existing web products. Genomic insight cards, action feeds, domain summaries, and conversation widgets — each surfacing GeneOps intelligence within your existing interface without a full product rebuild. Integration measured in hours, not sprints.
Every variant in the knowledge base that appears in the user's genotype file, with allele status, effect direction, confidence grade, and citation. Structured JSON — machine-readable and human-interpretable. The raw intelligence layer below all higher-level outputs.
Aggregated intelligence across 30 health domains — longevity, fitness, nutrition, pharmacogenomics, sleep, mental health, and more. Each domain profile reflects the combined picture of all relevant variants for that individual, not just single-variant findings.
Specific, actionable recommendations across supplements, nutrition, lifestyle, training, monitoring, and avoidances — each tied to one or more of the user's actual variants. Not population-level advice with a genomics wrapper — genuinely personalized actions that differ between individuals based on their specific allele combinations.
Beyond single-variant associations, the engine surfaces combinatorial gene–gene interaction effects — cases where two variants in combination produce an effect different from either alone. This layer captures the complexity that single-SNP analysis misses and is a core source of differentiation in the knowledge base.
Every output carries an explicit confidence grade — based on replication, effect size, and study quality. Partners can set minimum confidence thresholds for what they surface to users. The grade is always available for transparency, even when not surfaced prominently in the product UI.
Every finding is linked to its source publications via PubMed PMID. Partners can choose whether to surface citations in the user interface — for consumer products, clinical tools, or research applications that require direct access to the primary literature.
All 1477+ SNPs are dbSNP-verified and annotated in standard HGVS notation. Variant identifiers are consistently cross-referenced to ensure identity across 23andMe, AncestryDNA, and VCF file formats. The data model is designed for precision — not approximate matching.
The matching engine is fully deterministic. A given genotype file always produces the same output. There is no probabilistic inference, model drift, or session-dependent behavior in the analysis layer. Outputs are reproducible, auditable, and stable.
End-to-end encryption, strict access controls, and a GDPR/HIPAA-aligned data handling framework. Genotype data stays within GeneOps infrastructure — never shared downstream. The data architecture is designed around the sensitivity of genetic information from the ground up.
A polymorphic translation layer covers all content, actions, and UI strings. The system supports full localization without changes to the data model — launch in English, Swedish, or any language. Translation is built into the architecture, not bolted on as a post-deployment feature.
The full research database is publicly accessible at geneops.ai/research — every variant, every citation, every confidence grade, no login required.
We'll walk through the API documentation, sample outputs, architecture decisions, and what integration looks like for your specific use case and tech stack.