Mantisbiotech
Mantis builds behavioral forecasting infrastructure that creates a "digital twin" of an individual by continuously integrating physiological and behavioral data streams. The platform serves scoped, consent-based predictions to builders, enabling applications like pre-symptomatic infection detection and cognitive baseline monitoring. Mantis positions itself as a neutral data layer, similar to OAuth for behavior, rather than selling raw data.
- Healthcare Technology
- Data & Analytics
- Digital Health
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Human behavior is a chaotic system where small uncertainties compound, making raw behavioral data difficult to forecast. While tech incumbents like Google, Apple, and Meta each hold deep but siloed slices of an individual's digital record, none can assemble a holistic model of a person because their data is limited to their own products and monetized through narrow business models.
Solution
Mantis provides a behavioral forecasting infrastructure that assembles a "living model" or digital twin of a person from their continuous digital and physiological data streams. The company's thesis is that while raw behavior is chaotic, certain coarse-grainings—such as resting heart rate trends, sleep architecture, and adherence rates—are stable and highly forecastable. Mantis creates a unified record from user-granted data streams and serves scoped, revocable predictions to builders, acting as a neutral layer that does not sell data but sells the connection between consenting individuals and applications needing forecasts.
Target Audience
Primary customers are application builders and product teams that need forecasts of individual human behavior, including health diagnostics, wellness, and habit-related applications that require consented access to continuous behavioral and physiological data.
Features
- Integrates approximately 5,000 digital interactions per person per day and roughly 250,000 physiological measurements from a single smartwatch into a unified behavioral record
- Applies chaos theory principles to identify stable aggregates and routines, achieving up to 93% predictability for habit-anchored patterns like sleep location, commute, and spending categories
- Serves predictions through a scoped, revocable API model, where the person grants data streams and holds the keys, and builders receive limited, consent-based access
- Supports pre-symptomatic detection of infection from wearable physiology up to nine days before symptom onset
- Enables cognitive baseline monitoring to track slow drift and surface decline months before it would appear in a clinical visit