AI & predictions
AI XR Analytics
AI XR analytics is the application of machine-learning models to spatial telemetry to surface comfort issues, drop-off predictions and prioritised UX fixes inside immersive experiences.
It differs from query-only analytics: instead of forcing teams to ask 'which scene had the highest exit rate', the system surfaces the moments that mattered, with an explanation. This is the layer Gossip Analytics is built around.
AI XR analytics stacks combine anomaly detection on behaviour signals, forecasting on session-level cohorts and natural-language summarisation on aggregated results. The point is not to replace human judgement but to shorten the distance between telemetry and a decision small enough to fit into a sprint.
Why it matters
Immersive telemetry is dense and multi-dimensional. Without an AI layer, most product teams end up looking at 5% of what they collect. AI XR analytics is what makes the remaining 95% actionable — which is often where the highest-impact UX fixes live.
Example
In a VR onboarding, the AI layer flags scene 3 as the top drop-off risk based on comfort trends, hesitation clustering and completion drift versus previous releases. The team fixes scene 3's teleport logic before the release goes wide, avoiding a churn event they otherwise would have discovered post-launch.