Case Studies

What data-driven AI search looks like

What actually happens when AI-search decisions are made from measured demand, and what it costs when they are made from opinion. Each case study is written from real work, shows the method end to end, and is free to read online or download as a PDF to share with your team.

Latest

The 9% problem

What happens when 400 well-written AI prompts steer a content strategy, and nobody has measured the demand they are supposed to represent.

PDF + web · July 2026

400 AI prompts, measured against 182 days of real search demand

An agency delivered a 400-prompt workbook to steer a brand's AI-search content. We matched it against six months of Google Search Console data: roughly 5 million impressions, 5,000 distinct queries. The clusters the set covered carried roughly 9% of measured demand. The bulk of what people actually ask, prices, availability, technical data, and one topic with no page at all, was absent. The replacement: a per-page question engine that produced 14 content drafts, every one traceable to measured demand.

Read the case study Download the PDF
~9%
of real query impressions covered by the 400-prompt set
~5M
impressions
5,000
queries
14
drafts
In preparation

Next write-ups from the same engagements

These cases are delivered and measured; the write-ups are in progress. Each will appear here with a PDF download when published.

Write-up in preparation

The baseline before opinions: a multi-market AI visibility audit

How a measured baseline replaced opinion in a multi-market brand's AI-search strategy, and how a fix list ordered by impact came out of it.

Write-up in preparation

Why AI assistants love press portals

What makes press-portal content the kind AI assistants cite: server-rendered pages, factual answers, question-shaped headlines. The lessons, ready to copy for any content hub.

Write-up in preparation

A voice-of-customer radar that runs itself

How weekly customer-voice monitoring went from a manual chore to an automated pipeline: comments scored by an LLM, a dashboard rebuilt on schedule, and an integrity gate that blocks bad deploys.

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