Pinemarsh Consulting
AI Marketing Science Readiness Audit
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 offer
Test the analytics workflow before it becomes trusted infrastructure.
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.
For teams using
- →LLMs for marketing analysis, MMM, attribution, or experimentation
- →Internal analytics agents or coding agents
- →Vendor copilots, AI dashboards, or automated insight reports
- →Notebook assistants or reusable prompt workflows
Deliverables
What the audit produces.
20-40 marketing science eval cases
Run targeted cases against your current workflow, model, agent, notebook, copilot, or reporting process.
Failure-mode review
Inspect hallucination, causal mistakes, MMM misuse, attribution overclaiming, bad recommendations, and weak uncertainty handling.
Severity-ranked report
Produce a concise report that separates critical risks, important fixes, and acceptable limitations.
Implementation recommendations
Recommend controls, evals, workflow changes, review gates, and follow-on fixes that make the workflow safer.
Optional model benchmark
Benchmark the same cases against Claude, Codex, Pi, Azure-hosted models, or your current provider where access allows.
Failure modes
The review looks for the mistakes that matter in marketing science.
- → Causal claims without evidence
- → MMM interpretation errors
- → Attribution overclaiming
- → Experiment design mistakes
- → Confident but unsupported recommendations
- → Weak uncertainty language
- → Metric and data-lineage confusion
- → Poor handling of contradictory evidence
Inputs
What we need to inspect.
- — Agent or workflow transcripts
- — Prompts, skills, system instructions, or notebook templates
- — Representative reports or analysis outputs
- — Relevant marketing measurement context
- — Current model/provider setup where available
AI Marketing Science Readiness Audit
$6,000
Fixed fee for the standard readiness audit scope.
- ·20-40 marketing science eval cases
- ·Review of current workflow/model/agent outputs
- ·Severity-ranked findings report
- ·Implementation recommendations and control plan
- ·Optional benchmark quoted privately when needed
When this is not enough.
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 with the Marketing Measurement Audit →Ready to test the workflow?
30 minutes. We will identify the workflow, decide whether evals are feasible, and confirm whether this audit is the right shape.
Or email Gui directly: guilherme.diazberrio@pinemarsh.com