AI evaluation · Quality engineering
Evaluating enterprise AI applications
An AI feature that seems fine in a demo can fail quietly in production. Mentarium built evaluation for enterprise AI applications that scores their outputs against defined metrics, using AI as a judge alongside human review, so a team knows how a change affected quality before it ships.
Implementation scope
The evaluation set
Representative prompts and expected outcomes drawn from real use, covering the ordinary cases, the awkward ones and the requests the application should decline.
AI as judge
A model scores each output against the metrics that matter for the application: relevance, groundedness in the source material, task completion, tone and safety, with human spot-checks to keep the judge honest.
Comparing changes
Every prompt, retrieval or model change is scored against the same set, so the team sees what improved, what regressed and by how much before deciding to release.
What changed
Release decisions for AI features rested on measured quality rather than on a few manual tries, and regressions were caught before users saw them.