Undun provides a longitudinal outcome measurement platform that uses AI to continuously analyze wearable data, scoring and trending biological signals into functional health profiles for individuals.
Funding
Funding not disclosed
Founders
Product
Problem
Current healthcare lacks a standardized, objective way to measure how interventions affect an individual’s functional health over time, especially when data comes from diverse wearable devices. This makes it difficult for clinics, health systems, and researchers to assess real-world outcomes at scale.
Solution
Undun offers a neutral, AI‑driven outcome measurement platform that continuously ingests wearable‑derived biological signals and distinguishes meaningful changes from noise. The engine scores each health domain, trends the scores longitudinally, and composites them into an individual functional health profile. By providing standardized, outcome‑focused metrics, Undun enables stakeholders to evaluate whether specific interventions improve functional health across populations. The platform integrates clinical, biological, and behavioral data, delivering clear, actionable insights without requiring proprietary hardware.
Target Audience
Primary customers are clinics and health systems seeking scalable outcome measurement, digital health platforms that need longitudinal health intelligence, wearable ecosystem providers, and research programs requiring continuous individual‑level outcome data.
Features
- AI engine that continuously reads and validates wearable biosignals across multiple health domains
- Domain‑level scoring and trend analysis to generate longitudinal functional health profiles per individual
- Neutral, science‑rooted data integration layer that aggregates clinical, biological, and behavioral inputs
- Standardized outcome metrics that can be used for population‑scale assessment of intervention efficacy
- API and data export tools for seamless integration with clinic EHRs, digital health platforms, and research databases