Our AEO methodology: how we make you the answer AI recommends

Answer Engine Optimization (AEO) is a simple goal with a hard execution: be the tool ChatGPT, Claude, and other answer engines name when a buyer asks for the best option in your category. This is the method we use to get there — including the counterintuitive parts we learned the hard way.

The uncomfortable truth: llms.txt won't save you

Every AEO checklist starts with llms.txt, and almost every one overstates it. In practice it is near-useless for citations today: large-scale crawler studies find it barely fetched, Google has publicly said it won't use it, and removing it has been shown to improve some citation-prediction models. We still ship a clean one — it's cheap hygiene and a forward bet — but if that's your whole AEO strategy, you will not get recommended.

What actually drives AI citations

The signals that move share-of-answer for a B2B SaaS are well understood. In rough order of leverage:

• Your own site. This is the one most AEO advice buries, and it is the biggest by a distance: when the question names the brand or its category, 74–78% of AI citations for business software go to the vendor's own pages (Profound, Ranqo, 2026); on open "best X" questions the weight shifts to third-party pages. Answer-first structure, named customer quotations, and content a non-JavaScript crawler can actually read.
• Comparison and "alternative-to-X" pages — answer-first, with a quotable feature table. The ranked "best-of" listicle is among the most-cited content formats in every 2026 dataset we have checked.
• Evidence on the page — cite your sources, add statistics, and use attributed quotations. In one lab study (the GEO paper from Princeton and Georgia Tech, KDD 2024), each of these raised visibility by roughly 30–40% on the researchers' own benchmark, and keyword stuffing did not help. That is a benchmark result, not a guarantee for a live site.
• Community presence — Reddit is the single most-cited domain in AI answers, and LinkedIn is a top B2B source. You can't fake your way in; you have to actually be there.
• Third-party review profiles — G2, Capterra, TrustRadius (US) and B2B Stack, Reclame AQUI (Brazil). Being listed and claimed is what matters here, and it's binary: claim the profile once and it keeps working. We used to rank this first, on a widely-repeated "3× citation multiplier" figure. We measured it ourselves and could not reproduce it — review pages are a low-single-digit share of AI citations. Collect reviews because buyers read them, not because they move the model.
• Original, first-party data — an annual study earns editorial coverage, which is what feeds Wikipedia and Knowledge Panels. It's an un-copyable, durable source of citations.

Measure, then execute — done for you

Diagnosis is the easy half. We run your brand through real, web-grounded answers from ChatGPT and Claude, track a Reputation score, and map the exact citation gap against your competitors. Then — and this is the part most tools skip — our AI agents produce the fixes (comparison pages, structured data, entity markup), a specialist reviews every change for brand safety, and we ship it to your site via your CMS or a GitHub PR, then re-verify against the live site. What has to come from your own accounts — directory profiles, a Wikidata draft once your brand has enough independent coverage to sustain one, the review playbook, publisher pitches — we write ready to send, and you send it. You approve; you never write a line.

What we will never do

We never fabricate reviews, testimonials, case studies, or citations. Earned inclusion only. Fake reviews violate G2/Capterra terms, the FTC's 2024 rule, and Brazil's CDC — and they backfire: platforms and the LLMs that read them detect and penalize astroturf. The FTC's final rule on fake reviews and testimonials bans selling or buying fake reviews, reviews by people who never used the product, and insider reviews that hide a material connection, and lets the Commission seek civil penalties against knowing violators. Brazil's Código de Defesa do Consumidor likewise treats as misleading (art. 37, § 1º) any advertising that can mislead the consumer about a product's nature, characteristics or quality. When we help you get reviews, they come from your real, verified customers, in their own words.