Your movement data, queryable.

Every processed session becomes part of a knowledgebase: movement windows, their embeddings, derived features, and links back to the raw segments. Our own product reads from it — and so can an AI agent. Claude, ChatGPT, or a custom agent built in-house: they ask the questions their users ask, and the knowledgebase answers with numbers it can point to.

It is the second of the two channels the platform is built around.

Three conversations that should be possible.

Illustrative exchanges, written by us — labeled as such. The numbers are invented; the shape of the answers is the design goal.

Running coachExample
Coach

My athlete's left side looks off in the final kilometres. What does her gait asymmetry actually look like this month?

Agent

Across 12 sessions this month, stance-phase asymmetry averages 4.1% — up from 2.3% last month. The increase is concentrated in deceleration segments late in each run: 9 of the 11 flagged windows sit in the final third of a session. The linked segments show the pattern directly.

evidence · 11 windows · 12 sessions · feature: stance-phase asymmetry

PhysiotherapistExample
Physio

How has knee-loading asymmetry trended across the six sessions since return to running?

Agent

It has come down in steps: session means fall from 8.2% to 3.9%, with the largest drop after session three. Two windows in session five run against the trend and are linked for review. This describes movement-pattern change over time — it is not a medical diagnosis.

evidence · 6 sessions · 214 windows · trend: decreasing

Football analystExample
Analyst

Show me our number 8's off-ball movement patterns across the last 5 matches.

Agent

Three clusters recur: delayed runs into the half-space (23 instances), checks toward the ball carrier (17), and covering shuffles during opposition build-up (31). Against the previous five matches, deceleration quality in the second cluster declines; the 12 lowest-scoring windows are linked.

evidence · 5 matches · 71 windows · clusters: 3

Answers that carry their evidence.

Every answer is computed from stored movement windows, and links back to them. An agent that cannot ground a claim in the knowledgebase is expected to say so rather than improvise — the interface is designed to make the honest answer the easy one.