AI features fail differently from normal software. Instead of throwing errors, they get subtly worse on inputs nobody tested, in ways your monitoring won't notice for weeks.
Ordinary software tells you it's broken. An AI feature will keep answering confidently while getting steadily less useful, and only your users will know.
A fixed set of real cases scored on every change, so a prompt tweak that silently breaks three scenarios gets caught before it reaches anyone.
Quality, latency, and cost tracked per feature in production. A 200 from the endpoint tells you very little.
Alerting when the inputs you're getting stop resembling the ones you tested on, which is how AI features degrade without anything appearing to break.
Prompt injection defences, output filtering, and hard limits on what a model-driven feature can do with the permissions it's been given.
Prompts, models, and retrieval configuration versioned together, because changing any one of them changes behaviour and you need to know which.
Shipping a change to a slice of traffic, comparing it honestly, and reverting in minutes when the numbers say it's worse.
Most AI features in production today are changed by editing a prompt and watching for complaints. That works until it's serving enough people to matter.
This applies hardest to anything that takes action on its own (see AI agents), and it sits on top of a well-built integration layer.
If you've got an AI feature live and no way to tell whether it's getting better or worse, that's the gap we close. Send us what's running today.