Reality-Graded AI QA for Agency Recommendations and Client Reporting

An async audit-and-monitoring service for performance marketing agencies that use LLMs to draft client recommendations, performance explanations, test hypotheses, pacing forecasts, and next-step memos. Instead of trying to judge whether an ad creative “worked,” AE grades the truth-tracking quality of the agency’s AI-assisted claims: which recommendations predicted lift that never appeared, which explanations were post-hoc rationalizations, which forecasts missed badly, and which client-facing narratives overstated confidence beyond the evidence. Agencies submit a weekly sample of AI-assisted recommendations and reports plus the eventual observable outcomes: did the predicted CPA improvement occur, did the flagged risk materialize, did the proposed test beat control, did the forecast land within range. AE returns a reliability scorecard by workflow/model/prompt, using its six-pattern autopsy taxonomy to show where reasoning broke, and applies promotion/demotion/kill rules to agency AI workflows. This is not ad analytics or A/B testing software; it is behavioral QA for the epistemic layer wrapped around campaign decisions and client communication.

As marketing agencies industrialize AI-generated strategy and reporting, AE offers a distinct and timely control layer that audits whether those claims actually tracked reality, making it a credible high-value niche service.

Engine verdict: GATHER_MORE_SIGNAL (WORTH_SKIMMING). Real wedge, but truth-linked agency QA is unproven until agencies both pay and provide clean falsifiable claim-to-outcome data.