Athlea runs on a knowledge graph built for one subject: sport and wellness science. This page is that graph, open for you to poke at.
Most training apps wire a general-purpose AI model to your data and call it intelligence. There is no separate body of knowledge underneath — nothing that constrains what the model is allowed to tell you about your training.
Athlea is built the other way round. Published sports science is structured into a graph first, and answers are traced through it. The same graph you can explore here is the one that shapes what the app tells you.
Pick a question and see how entities connect across domains — the same traversal logic that drives real-time recommendations.
The common pattern is to connect a general-purpose model to your training data and ship it. That model knows a little about everything and nothing in particular about sport science. Nothing sits underneath it to constrain what it says.
Athlea has a dedicated sports and wellness knowledge graph: published research structured into entities, the relationships between them mapped, and stronger study designs weighted above weaker ones. Answers are traced through that structure rather than recalled from a model's memory.
It is a known property, not a rumour: language models generate plausible text, and plausible text can be confidently wrong. Grounding answers in a structured body of evidence does not eliminate that — we are not claiming it does — but it constrains what can be said and gives you something to check it against.
General AI assistants can cite pages they have browsed. What none of them expose is a curated, domain-specific evidence structure that the answer is actually traced through. Athlea shows you both: the answer, and the path it took to get there.