Answers that cannot be verified.
With no source cited, nobody can defend the result to a client or an auditor.
AI by product phase · The AI inside
AI goes in where it multiplies the product and stays out where it gets in the way. Every system with its source, its cost limit and its way of checking that it still works well.
With no source cited, nobody can defend the result to a client or an auditor.
Model calls with no limit and no measurement. The bill shows up at month end.
The model changes, quality drops and nobody notices until a client complains.
Model-agnostic by design: if tomorrow there is a better or cheaper one, it gets swapped without rewriting the product.
They query your system, not their memory. An answer with the source that supports it.
Routing, logging and limits per function. Visibility of the spend before the invoice.
A set of cases run on every change. If quality drops, it fires before production.
A log of automated decisions, GDPR and the AI Act considered from the design.
What each one does, with which data and what limits it has.
Consumption per function and per client, live.
Real cases that verify quality on every release.
Everyone says "we use AI". Almost nobody explains how it decides. That difference, between a black box that spits out answers and a system you can audit, is what separates a reliable tool from a legal risk with good marketing. This is what auditable AI really means.
An AI agent is not a chatbot. It is an autonomous system with a bounded role, controlled access to tools and an auditable log. This is the honest definition, no hype.
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