AI Data Analyst Agent that turns events into decisions

For product teams that want analytics tied to product decisions, David covers the loop end to end. For enterprise-scale attribution, billions of events, and mature predictive cohort models, Mixpanel and Amplitude remain stronger. Many teams use both: Atoms for the decision loop, a dedicated platform for deep historical analysis.

No. David designs the events and Alex wires them into the code during the same task. Tracking ships with the feature, not weeks later when someone files a ticket.

Yes. You ask David in plain English what you want to learn. He answers with a Notebook analysis, not a chart you have to translate into a decision.

David runs lightweight predictive analyses in Notebooks. For mature propensity models and ML-driven cohort prediction, Amplitude and Mixpanel remain stronger.

David surfaces an insight, writes it as a recommended change, and hands it off to Emma or Alex as a scoped task. You see the decision and the change side by side instead of a dashboard with no follow-up.

David works primarily with data captured inside your Atoms project. For pulling from external warehouses, you can export and analyze in a Notebook block, but native BigQuery connectors are not built in today.

Volume limits depend on your Atoms plan. For most builder and small-team use cases, the default capacity is sufficient. For very high-volume products, a dedicated analytics platform may still make sense.

You can export events and analyses for use outside Atoms. Native warehouse pipelines are not built in today, so for ongoing warehouse sync, pair Atoms with a dedicated ETL tool.