What we find when buyers ask AI before they ask you.

Regulated lending

Competitor listed first in every comparison. Then the compliance gap.

A lender appeared in most answers about its category but was recommended in far fewer. The sources the engines relied on were independent comparison guides that listed competitors first. The action was to earn a place in those guides, not to write more content on the brand's own site.

A second pattern ran alongside this one. Legal-approved content had been written to hedge and disclaim everything, which stripped it of the specific, attributed facts that AI engines cite. Disclosure is a requirement; vagueness is not. Precise compliant facts, attributed to named experts, are citable. Vague disclaimers are not. The team updated two key product pages to include exact figures with named credentials. Re-measurement showed the recommendation rate moving, where chasing more mentions alone had not.

Guide: LLM SEO and earning mentions ->

Insurance

Strong on one engine, absent on another

An insurer appeared clearly in one assistant and barely at all in another, because the two engines drew on different sources. Measuring a single surface would have hidden the gap entirely. The plan targeted the sources behind the weaker engine. The next scan confirmed the brand began appearing there.

Guide: earning a place in AI Overviews ->

Consumer brand

Visible in answers, but not ready for the agent

A consumer brand appeared in AI answers but failed the bar an AI agent needs to act on a buyer's behalf: structured product data was missing and key details were inconsistent across pages. Separating visibility from execution let the team fix the actual problem rather than write more marketing copy.

Guide: AI shopping readiness ->

Multi-product

A different result per product line

A brand with several product lines found one line was recommended consistently while another was absent from answers entirely. A single brand-level score would have averaged the two into something misleading. Measuring per product showed exactly which line needed work and why.

Guide: the measure-act-remeasure loop ->