AI Platform

M.A.I.D.: Microbiome. AI. Data.

An AI platform that generates test-ready data from synbiotic metadata — built to serve both pharma R&D and wellness product design.

Overview

From synbiotic metadata to structured, test-ready data.

M.A.I.D. is our AI discovery engine for synbiotics, precision biotics, and drug candidates. It ingests metagenomic, clinical, and literature data into one knowledge graph, then uses AI to generate test-ready data, predicting how a strain or formulation is likely to behave before it ever reaches the bench.

Ingest

Metadata In

Strain genomics, prebiotic pairings, and study metadata feed the graph continuously.

Model

Function Predicted

Metabolic flux and interaction models estimate how a community will actually behave — before the bench.

Generate

Test Data Out

Structured, AI-generated test data ready for R&D review or product formulation.

Two Audiences, One Platform

Built for pharma and wellness partners alike.

For Pharma

Accelerate R&D

  • AI-generated test data narrows candidate pools before costly lab work begins
  • Real-world engraftment and outcome signals reduce clinical guesswork
  • Measurable clinical outcomes, not just modeled predictions
  • Structured, auditable data trails support regulatory readiness
  • Speed and cost improve together, not at each other’s expense
For Wellness Partners

Sharper Products, Faster

  • Data-driven recommendations for formulation and positioning
  • Feedback loops from real consumer usage refine the next generation
  • Faster iteration cycles without running a full new study each time
Data Network

The dataset grows every time someone uses one of our products.

Large population health data, correlated in real time with the microbiome, gathered from individuals tracked longitudinally over several years. The network gets more valuable, and more predictive, with every cycle.

Collect

Large population health data gathered through synbiotic users and wellness partner channels, tracking individuals longitudinally over several years.

Enrich

Each result feeds back into the knowledge graph, densifying it over time.

Apply

Enriched data sharpens the next product cycle and strengthens what we can offer partners.

Curious how this could work with your pipeline?