AI Evaluation

Wherefore AI

“AI evaluation, operationalized”

Most enterprise AI deployments are evaluated by feel: occasional spot checks, anecdotal feedback, and a general sense of whether it seems to be working. Wherefore AI replaces that with a systematic evaluation operating system: continuous output capture, structured scoring, and regression detection before it becomes a problem.

The Problem

•AI output quality degrades over time: model updates, data drift, prompt changes.
•Spot checks and anecdotal feedback don't scale across multiple AI programs.
•Nobody knows which AI outputs are being acted on, and which are being ignored.
•Regressions aren't caught until they show up in business metrics. Too late.
•Compliance requires evidence of evaluation, not just assertions of quality.

What It Does

Output capture: every AI response logged with context, timestamp, and input.
Structured scoring: evaluate outputs against configurable criteria (accuracy, tone, policy compliance).
Performance trending: track quality over time, by model, by use case, by team.
Regression detection: automatic alerts when quality drops below threshold.
Evaluation workflows: human review queues for outputs that need judgment.
Audit trail: full record of what was evaluated, by whom, and what was decided.

Who It's For

AI Program ManagersMLOps TeamsHeads of AI QualityCompliance and Risk OfficersEnterprise AI Leads