Sourcing List: AI-Native Personal Health × Continuous Biomarkers
- January AI — Predicts glycemic response to foods using CGM + ML, now largely without requiring a sensor. Fits: pioneered "digital twin" glucose modeling; raised from Felicis/HAND Capital; pivoting to AI-first (sensor-optional) is a signal of where the category is going.
- Signos — CGM + AI platform for metabolic health and weight management in non-diabetics. Fits: Series B, partnership with Dexcom (strategic investor), clinically oriented go-to-market rather than pure consumer wellness.
- Empirical Health — AI-driven proactive primary care built on continuous wearable data (Apple Watch heart data, etc.). Fits: early-stage, founded by ex-Cardiogram team, physician-in-the-loop model on top of passive biomarker streams — genuinely AI-native care delivery.
- Superpower — Membership-based "digital clinic" combining 60+ lab biomarkers, wearables, and AI health concierge. Fits: seed/Series A stage, strong consumer traction and waitlist momentum; interesting as a full-stack aggregator of biomarker data.
- Mito Health — AI-powered longevity/health platform analyzing bloodwork and biomarker panels for personalized protocols. Fits: early-stage (Singapore/US), lean team, positioned as lower-cost AI-first alternative to concierge longevity clinics.
- Sahha — API/SDK turning passive phone and wearable sensor data into digital biomarkers (sleep, activity, mental health scores) for other apps. Fits: pre-seed/seed infrastructure play — picks-and-shovels for the whole thesis.
- Ultrahuman — Smart ring + CGM ("M1") ecosystem with AI-driven metabolic and recovery insights. Fits: better known, but notable for being one of the only companies unifying ring + glucose data in one platform; strong revenue growth out of India/UAE.
- Nutrisense — CGM subscriptions paired with dietitian + increasingly AI-driven nutrition insights. Fits: bootstrapped-to-scale consumer traction, large proprietary dataset of glucose-response-to-meal pairs — a data asset play.
- Levels — Metabolic health app layering interpretation on CGM and lab data. Fits: the category-definer; a16z-backed, worth tracking for its shift toward broader biomarker panels and AI food logging even if past "early."
- Junction (formerly Vital) — API unifying wearable, CGM, and at-home lab data for health companies. Fits: seed/Series A infrastructure; nearly every company on this list needs this pipe, and it has YC/Point Nine backing.
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White-space gaps:
- Non-glucose continuous biomarkers. The field is massively over-indexed on CGM. Continuous lactate, cortisol, ketones, hydration, and inflammatory markers (companies like Biolinq and PKvitality are working on hardware) have almost no AI-native software layer yet — the "Levels for cortisol" doesn't exist.
- Longitudinal multi-modal fusion for early disease detection. Plenty of companies score today's sleep or glucose; almost nobody is building models that fuse rings + CGM + labs + genomics over years to flag pre-clinical disease drift (early insulin resistance, arrhythmia risk, perimenopause transitions). The data pipes (Junction, Sahha) now exist — the interpretive layer with clinical-grade validation is wide open.
Note: all companies listed are real as of my knowledge cutoff; stage details should be re-verified before outreach, as this space moves fast.