The problem
The first question from a new customer is how much history they need before the predictions are worth using. A demo that runs on years of data cannot answer it, and a vendor's promise is not an answer either.
The question has a measurable answer per field. Some fields are predictable from a few weeks of data; others need months. Knowing which is which decides what to automate on day one and what to keep in review.
How it works
The view reruns _evaluate with the history limited to orders up to a cutoff, so each slider position answers "what would a tenant with only this much data get today?" Because the cutoff is a condition on the query rather than a retrained model, moving the slider is another query, not another training run.
The same check runs in a real onboarding: evaluate on the history the customer has, and start automation on the fields that already clear the bar.
For the full architecture, see the technology overview. For the broader narrative across multiple use cases, read The Predictive Application.
See it live
This use case runs in the 📋 ERP demo today. Click through to the live application and inspect the queries that produce the result.
