Performance Lab
AI-assisted product performance analytics
A studio framework for pairing model-generated summaries with human accountability inside application analytics work.
What “assisted” should mean
In our lab, assistance means a model can draft anomaly lists, cluster similar session paths, or propose wording for a stakeholder memo. It does not mean the model owns the causal story or the roadmap decision.
Teams practising AI-assisted product performance analytics with Aianalytics learn to keep a visible audit trail: prompt, output, human edit, and final claim.
Lab sequence
Four stations we use with cohorts
- Signal intakeCurate the week’s events before any model sees them — reduce garbage-in summarisation.
- Assisted scanRun structured prompts that ask for contradictions and missing segments, not just highlights.
- Human adjudicationMark each claim as confirmed, deferred, or rejected with a one-line rationale.
- Decision packagingShip a memo that shows where AI helped and where judgement overruled it.
Practise the lab inside a full programme
Product Signal Intelligence embeds these stations across modules five and six.