A focused audit for teams using AI, LLMs, or agents in marketing analytics. We test whether the workflow can handle marketing science reasoning before its outputs reach clients, executives, or budget decisions.
Diagnostic engagement. Not an agent build.
The problem is not only that LLMs hallucinate. The sharper risk is that weak marketing measurement assumptions become faster, cleaner, and more persuasive when wrapped in agents, copilots, notebooks, or automated reports.
This audit runs marketing science eval cases against the actual workflow and reviews the outputs like a senior measurement lead: what failed, why it matters, and what control would reduce the risk.
Run targeted cases against your current workflow, model, agent, notebook, copilot, or reporting process.
Inspect hallucination, causal mistakes, MMM misuse, attribution overclaiming, bad recommendations, and weak uncertainty handling.
Produce a concise report that separates critical risks, important fixes, and acceptable limitations.
Recommend controls, evals, workflow changes, review gates, and follow-on fixes that make the workflow safer.
Benchmark the same cases against Claude, Codex, Pi, Azure-hosted models, or your current provider where access allows.
If the issue is broader than AI workflow readiness, use the full Marketing Measurement Audit instead. That covers the measurement stack: attribution, MMM, experiments, tracking, planning cadence, and AI-assisted analysis where relevant.
Compare the full audit30 minutes. We’ll identify the workflow, decide whether evals are feasible, and confirm whether this audit is the right shape. You’ll speak with Gui directly — not a sales team.
Book a free 30-minute discovery call. No forms, no pitch deck — just a scoping conversation.
Book a discovery call