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An AI-ready biomarker data layer that transforms lab results in any format into structured, normalized and female-contextualized data for digital health and AI platforms.

Digital health and AI platforms increasingly rely on laboratory data, but current systems use generic reference ranges, ignore cycle phase, hormonal status and lifecycle stage, and interpret biomarkers in isolation. This creates incorrect assumptions and reduces the reliability of insights and recommendations for women.

The project provides an infrastructure layer that transforms lab results in any format into structured, machine-readable biomarker data. It normalizes biomarker names, standardizes units, applies a unified schema and adds women-specific interpretation based on physiological and hormonal context. Biomarkers are analysed as connected patterns rather than isolated values, with results delivered through API and MCP integrations.

The target customers are femtech platforms, digital health companies and AI products that use biomarker data as a core part of their services. The business model combines usage-based API pricing, SaaS subscriptions, MCP connector licensing and enterprise contracts.

The initial target market includes approximately 200–500 relevant companies, while the broader global market for lab-data structuring and analytics is estimated at €2–5 billion. The project has a working product and early commercial validation across B2C and B2B use cases, including an enterprise integration and more than 120,000 biomarkers processed. The current focus is on pilot integrations, recurring API usage and scaling commercial partnerships.

Stage: Early-stage
Geo: United States