AI-visibility audits and fixes — how brands show up, or don't, across ChatGPT, Perplexity, Gemini, and AI Overviews.
Supplement brand
When asked to compare options in its category, AI answer engines consistently omitted the brand — competitor names surfaced instead, even on branded-adjacent queries.
Restructured product pages with clear comparison tables and ingredient-level specificity, then added dedicated FAQ schema so models had citable, structured answers to draw from.
Full audit available to clients.
Skincare brand
Product claims on-site weren't phrased in a way models could extract confidently, so AI Overviews defaulted to third-party review sites instead of brand-owned language.
Rewrote key product copy into direct, quotable claim-and-evidence pairs, and added a plain-language ingredient glossary to reduce model uncertainty.
Full audit available to clients.
Home fitness equipment brand
Long-tail queries like "best for small spaces" returned no mention of the brand at all — no content on-site was ever mapped to that use case.
Built a use-case content cluster addressing space, noise, and budget constraints, interlinked to product pages with matching structured data.
Full audit available to clients.
Specialty coffee brand
Model responses cited outdated pricing and discontinued products, pulling stale information from an old sitemap that had never been cleaned up.
Audited and pruned the sitemap, added last-modified signals, and resubmitted canonical product data to cut reliance on cached, outdated sources.
Full audit available to clients.
Pet food brand
Ingredient-safety questions routed users to forum threads and unaffiliated blogs rather than the brand's own answers, despite the brand holding the more accurate information.
Published a structured, sourced ingredient-safety hub with clear authorship and citations, giving models a stronger, more authoritative source to draw from.
Full audit available to clients.