We talked about how to evaluate suppliers based on their ability to demonstrate fast and sustained value, with a milestone-based plan, a flexible architecture for changing models, and a team that maintains what has been implemented.
We analyze how the cost of AI is controlled from the design stage by choosing between token-based or license-based consumption, setting limits per user, reusing repeated responses with caching, and validating that the business case closes with the maximum monthly expenditure.
We'll tell you why governing data in the AI era requires going beyond data quality. It involves governing the data itself, the model, biases and regulations, and the conversations and documents an agent produces. Why, if the model isn't governed end-to-end, it won't reach production.
To determine if an AI is still performing well, observability is essential: automated metrics for response quality, resource consumption, biases, and model availability in production. We'll explain how to monitor model degradation over time.
What changes when a model must answer to a regulator? What kind of metrics can be used to assess fairness and potential biases? What information should be prepared before an audit?
We explore why there can be significant differences between proposals that address the same need, what aspects are not shown in the price and implementation timeframe, what the main selection criteria are, and why choosing a technology partner involves looking beyond the immediate project.
