Layer 1 — DATA

Meaningful data for prediction

The data layer combines multiple kinds of evidence that are rarely connected at the same depth: human multi-omics, food chemistry, wearables, building-level exposures, urban environmental models, and detailed records of interventions. The purpose is not to create more data in the abstract. The purpose is to create data that is meaningful for prediction.

Layer 2 — AI

Modelling response, not just state

The AI layer is designed to model biological response rather than only static state. In practical terms, this means learning how different foods, formulations, conditions, timings, and environments translate into differentiated responses across different humans. The platform’s ambition is to become progressively better at prediction as the evidence base becomes richer and more diverse.

Layer 3 — Systems

Operational, not only descriptive

The systems layer is what makes the platform operational rather than descriptive. It includes the workflows, software, and, later, the actuation systems that allow interventions to be delivered, measured, refined, and repeated. Over time, this may include formulation, food preparation, environmental tuning, and institutional protocols.

Architecture

Data Layer

Multi-omics · Food chemistry · Wearables · Building exposures · Urban environment

AI Layer

Predictive biological response models · Differentiated population learning

Systems Layer

Workflows · Software · Actuation · Formulation · Environmental tuning

The Loop

Measure

Model

Test

Improve

different from generic health AI

Richer evidence, not just more data

Generic health AI often relies on retrospective records, simple sensor streams, or narrow clinical endpoints. Evolve is oriented toward richer evidence: not just what happened, but what was consumed, what environment existed, what the body did next, and what intervention can be tested in response.

See it in action

Athens v1 is already activating the platform’s first operating loop.