Lost traffic to AI Overviews?

Three timelines stack: schema + llms.txt + content rebuild ships live inside 4 weeks (Recovery Sprint), first AI citations across ChatGPT, Perplexity, Gemini, and Claude typically appear 4–8 weeks after deploy for cited-brand candidates, organic click recovery shows meaningfully at 90+ days as both traditional Google rankings and AI-engine citations compound. Brands with existing entity footprint (Wikipedia, Wikidata, third-party mentions) recover faster; new brands compound 3–6 months in.

Opposite — AEO-structured content typically lifts traditional Google rankings too. Clear TL;DR summaries, structured Q&A, citable original stats, authoritative author bios, and well-formed Schema graph all signal quality to Google's helpful-content classifier as much as they signal quotability to ChatGPT and Perplexity. Most clients see Google rankings improve in parallel with AI citation density during the recovery sprint, not in trade-off. We track both surfaces and report month-over-month.

No — and any agency that does is misleading you. AI engine citation depends on multiple factors outside our control: query competitiveness, your existing entity footprint, third-party mention graph, category authority signals, and the engines' own selection algorithms (which change without notice). What we guarantee is process: schema deployment, llms.txt drafted from real content, top 20 pages rebuilt for AEO, entity SEO work where eligible, AthenaHQ + Profound tracking live, and monthly reporting on what is and is not being cited.

We rebuild your top 20 commercial-intent pages in the 4-week Recovery Sprint ($4,499) — these account for 60–80% of AI-Overview-affected revenue at most brands. Ongoing rebuilds (next 80 pages over 4–6 months) run on the AEO Retainer ($1,799/mo, 16 hrs/month). For faster cadence, Multi-Channel retainer ($5,999/mo) handles 8–12 page rebuilds per month plus paired SEO + content work. We prioritise by Search Console click-loss data, not by your guess at which pages matter.

Yes — AEO architecture works identically in non-English markets across ChatGPT, Perplexity, Gemini, and Claude. We have multi-language content strategists in pool covering Spanish (LATAM + Iberian variants), French (France + Quebec), German, Dutch, Portuguese (Brazil + Portugal), Italian, Hindi, Mandarin, Japanese, and Arabic. Latin-script languages work cleanly today; CJK and Arabic work with slightly reduced parsing accuracy on source attribution but improving fast. Schema + llms.txt are language-agnostic.

Yes — llms.txt is read by some AI engines as of 2026 (Perplexity, You.com, Brave Search AI, several Cohere-trained models), and adoption is expanding. The bigger value beyond direct reading is the entity graph it implies: a well-structured llms.txt + llms-full.txt forces you to organise your content as canonical answer-engine surface, which produces benefits even before universal adoption. Plan for it to matter more, not less. We refresh llms.txt quarterly as your content evolves.