A model that works well at launch can perform worse months later without anyone noticing. Monitoring makes quality, speed and cost visible, so you can adjust before your users notice.
Three signals that together show whether an AI feature is doing what it should.
Scores on a fixed test set and samples from real usage. This shows whether the model gets better or worse after a change.
Response times per feature, so a slower model or a busy provider doesn't quietly slow down your product.
Cost per feature and per user, with an alert as soon as it deviates from what's expected.
Monitoring only works if someone looks at it. That's why it comes with a fixed review moment alongside the dashboards.
Which outcome matters to your users, and how do you measure it?
A fixed set of examples from your practice, that grows with new usage.
Quality, speed and cost in one place, with alerts on deviations.
Periodically reviewing what's changed and what needs adjusting.
In the quickscan I look at your current setup and what's missing to make quality and cost visible.