Getting an AI feature to work in a demo takes days. Keeping it fast, accurate and affordable when hundreds of people use it every day is a different job. Here is what we put in place for every system we run.
Watch the cost of each task
Model usage is billed per request, so costs grow with success. Measure the cost of each task, cache answers that repeat, and send simple requests to smaller, cheaper models. Budget alerts prevent surprises at the end of the month.
Monitor quality, not just uptime
A server can be up while the AI gives poor answers. Log every request and response, review samples regularly, and run your test set again whenever you change a prompt, a model or a data source.
Plan for failures
- 01A provider slows down or changes a model: switch to a fallback
- 02A request fails: retry, then hand the case over to a person
- 03Your data changes: re-index and re-test automatically
Keep security and data in check
Give each component only the access it needs, store secrets properly and know where your data is processed. When regulations require it, keep the whole system in your region or on your own servers.
Running an AI prototype that now needs to become a reliable service? We can review your setup in a free audit.