Answer Engine Optimization: Get Cited by AI

Answer engine optimization (AEO) is the practice of structuring content so that AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Bing Copilot — can extract it, verify it against other sources, and cite it when generating answers. Classic SEO earns a ranked link; AEO earns a citation: your company named inside the answer itself. The work centers on extractable passages, third-party corroboration, and structured data built for machine retrieval.

AEO is one discipline inside our generative engine optimization practice. Same standard, narrower aim: get cited, prove it.

Answer engines compress ten results into one paragraph. Most queries now end without a click. Your content can inform an AI answer and never be named in it — because the passage was not extractable, the claim was not corroborated, or the entity was ambiguous. Traffic charts flat-line while competitors get quoted.

That is the gap answer engine optimization services close. Not more content. Content the machines can lift cleanly and defend.

Two levers. Both mechanical.

Extractable passages. Engines quote what they can lift whole: two to four sentences, subject named explicitly, no dangling pronouns, one claim per passage, under a question-shaped heading. We rewrite your key pages into this form — human-readable, machine-liftable.

Corroboration. A claim that exists only on your own domain is, to a model, a rumor. Engines cross-check. We build corroboration networks: legitimate third-party placements, consistent entity data, verifiable facts repeated across independent sources. No fake reviews, no sockpuppets. Corroboration that survives scrutiny is the only kind that survives retraining.

Google's answer surfaces pull from its index, so crawlability and structure still govern. We deploy schema — Organization, Service, FAQPage, Article — that disambiguates who you are and what each page answers. We verify AI crawler access (GPTBot, Google-Extended, PerplexityBot) and ship an llms.txt file that routes models to your strongest pages.

Honesty clause: nobody can guarantee placement in AI Overviews or AI Mode, including us. What we control is the inputs — and a measurement loop that shows whether they are moving.

Blankpage runs answer engine optimization services in three phases. First, a two-week Diagnostic Sprint: we build a prompt panel of 100-200 buyer-relevant questions, run it across ChatGPT, Perplexity, Gemini, and AI Overviews, and record baseline citation share — the percentage of prompts where an engine names you — plus the exact sources each engine leaned on.

Then Design & Build, six to twelve weeks: passage rewrites, schema deployment, llms.txt, corroboration placements, entity cleanup. Every change maps to a prompt it should move.

Then the loop. Same panel, every month. Citation share, source attribution, drift. If a change did not move an answer, we say so and change course. Measured monthly is not a slogan. It is the deliverable.

Everything: the prompt panel and baseline data, rewritten pages, schema code, the llms.txt file, the monitoring dashboard, and a playbook your team can run without us. Full IP transfer. Clean exit. Three engagement models, no lock-in.

One boundary worth naming. AEO earns citations for what is true and yours. If engines are repeating something false — seeded reviews, fake comparisons, a poisoned data void — that is a different job: LLM poisoning defense. We do both. We do not confuse them.