Technical Trust Asset

AI Evaluation Methodology

How we prove AI quality before and after launch.

Metrics first

Every engagement defines success metrics up front — accuracy, resolution rate, cost per task, latency.

We build and maintain evaluation sets from real data so model quality is measured, not assumed.

After launch, models and agents are evaluated on an ongoing basis with drift detection and regression gates.

Uncertain outputs route to humans for review, and those reviews feed the next evaluation round.

What if the model underperforms?

We report measured performance against targets and iterate — evaluation gates prevent bad models from shipping.

How do you measure ROI of AI?

Business metrics (hours saved, tickets resolved, conversion) are tracked and reported alongside technical metrics.