Answer engine optimization is the practice of structuring content, schema and entity signals so answer engines like ChatGPT, Perplexity and Google AI Overviews cite your brand directly in their answers. Our AEO services build the machine-readable footprint those engines lift from, then track the citations you earn each week across every major model.
An AEO agency prepares your site so answer engines can quote it. That means one clear answer per page in the first 80 words, question-shaped H2s a real person types into a search box, and JSON-LD schema that names entities the model can trust. According to a Semrush study published March 2024, pages with FAQPage and HowTo schema were 38 percent more likely to be lifted into AI Overviews. We build that structure into every page we ship, then measure how often the four largest models name you.
We add Organization, LocalBusiness, Article, FAQPage and Service JSON-LD tuned to your business, and connect every entity with sameAs links to Wikipedia, Wikidata, LinkedIn and Crunchbase.
LLMs use entity graphs to decide who to trust. A brand with clean sameAs links and rich schema is 3x more likely to be named by an answer engine, per an Ahrefs study published January 2025.
We rewrite your most important pages so the first paragraph answers the reader question in 40 to 80 words, and every H2 is phrased as a question a customer would type.
Answer engines lift the shortest passage that satisfies the query. When your page opens with the answer, it becomes the passage the model quotes.
We run a weekly panel of 100 to 500 prompts through ChatGPT, Claude, Gemini and Perplexity and log every time your brand is named, linked or ignored. You see a dashboard, not a slide deck.
You cannot fix what you cannot measure. Tracking prompt share gives you a real number to move, week over week, not vanity impressions.
We give every article a named human author with a credential line, a bio page, and sameAs links to their LinkedIn and X profiles.
Google's March 2024 core update raised the weight of named-author signals. Anonymous content gets ignored by both Google and the LLMs that train on it.
Your firm challenges LLM biases by establishing a definitive cryptographic node in the global knowledge graph. We draft, submit, and monitor Wikidata statements and Wikipedia references to solidify your brand as a recognized entity. This program forms the ground-truth relationship mapping that AI search engines query directly when verifying your brand authority and executive footprints.
AI engines do not just scrape text; they resolve entities using structured knowledge bases like Wikidata. Securing a node here prevents LLMs from hallucinating your brand data and guarantees your inclusion in high-trust citation pools.
We convert your raw video assets and podcasts into LLM-readable formats. Your team receives structured, timestamped transcripts, video schema payloads, and semantic anchor points optimized for OpenAI Whisper and similar speech-to-text parsers. We align this conversational metadata with your core brand entities to capture real estate in multimedia AI search results.
LLMs increasingly ingest rich media directly to answer user queries. By structuring your audio and video files with precise semantic markers, you allow crawlers to extract and cite your conversational insights without relying on text-only pages.
average lift in AI citations after 90 days
We run 250 real buyer prompts through the four largest engines and record every brand named, source cited and gap where you should have been quoted.
Organization, LocalBusiness, Article, FAQPage, HowTo and Product JSON-LD, plus sameAs links to Wikipedia, Wikidata, LinkedIn and Crunchbase for every named entity.
Top 20 pages rewritten so the first 80 words answer the query, every H2 is a question, and every claim carries a named plus dated source.
8 to 12 new question-shaped pages per quarter, plus outreach to the exact sources the audit showed your target engines already trust.
A dashboard, not a slide deck. Prompt-share, competitor share, source mix and week-over-week movement per engine.
Answer engines run a retrieval-augmented generation loop. The model turns the user question into a vector, pulls the top passages from its index (a mix of live search and pretrained corpus), then writes an answer grounded in those passages. Brand selection is not random. The model favors passages with a clear direct answer in the opening lines, unambiguous entity markup, a named author, and a source domain it already trusts. An Ahrefs study published January 2025 found pages with linked Organization and Person schema were cited 3.1 times more often than unmarked pages in Perplexity answers. The mechanism is boring: reduce ambiguity, and the model reaches for you first. That is why our first pass is always schema and entity cleanup, not content volume.
Days 1 to 14 are diagnostic. We run 250 real customer prompts through ChatGPT, Claude, Gemini and Perplexity, log every brand named, and map the sources each engine cited. Days 15 to 30 are structural. We rewrite the top 20 pages to lead with a 40 to 80 word direct answer, add FAQPage and HowTo schema, and link entities to Wikidata. Days 31 to 60 we publish 8 to 12 answer-first pages targeting the exact question phrases your audience types. Days 61 to 90 we earn third-party mentions on the sources the audit showed the models trust in your category. First citations on branded and low-competition prompts usually appear by day 45. Non-brand answer share compounds from month 3 onward.
Prioritize by prompt-share opportunity, not by current Google traffic. Comparison pages, category definitions, pricing FAQs, and any URL that answers a question phrased with what, how, why, best or vs will produce the fastest lift. A BrightEdge analysis published February 2025 found 62 percent of AI Overview citations came from pages built around a single explicit question. Our sequence is: comparison and vs pages first, then category definition pages, then pricing and objection FAQs, then service and location pages, then blog. Skip generic home and about pages in the first pass. They rarely map to a prompt a real buyer types, so the citation lift is small.
Buyers already ask ChatGPT and Perplexity to name the top three vendors in your space. If you are not one of them, the pipeline shrinks before you get a chance to rank.
Impressions hold, clicks drop. AI Overviews are absorbing the answer. AEO is how you get quoted inside that answer instead of scrolled past.
No Wikipedia page, thin LinkedIn, no named authors on the site. LLMs cannot verify who you are, so they name a competitor who they can.
Last updated: 2026-07-15
SEO earns rankings in the ten blue links. AEO earns citations inside the AI answer that sits above them. SEO wins clicks. AEO wins the answer itself, whether the reader clicks or not.
First citations usually appear inside 30 to 60 days on branded and low-competition queries. Non-brand answer share compounds over 3 to 6 months as we ship more question-shaped pages and earn third-party mentions.
ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews. Each has a different retrieval pipeline, so our schema, content and off-page tactics are tuned per engine, not one-size-fits-all.
We operate as a specialized layer rather than a replacement. Your in-house SEO team continues to manage core site health and keyword targeting, while your PR team drafts brand narratives. Our role is to translate their outputs into machine-readable entity schemas, optimize your digital footprint for LLM ingestion, and supply the precise structured formats that search engines require to cite your assets.
We bypass generic keyword trackers in favor of custom APIs, proprietary LLM prompt scrapers, and specialized semantic analysis tools. Our stack monitors citation frequency, sentiment variations, and attribution share across ChatGPT, Claude, Gemini, and Perplexity. You receive raw mathematical data showing how often your brand is recommended relative to your competitors, along with the source URLs the models pull from.
If you pause your campaigns, your structured schema and existing citations remain online, but your influence will systematically decay. LLMs continuously update their weights based on the freshness of web data. When competitors publish newer, highly optimized semantic content, the models will slowly replace your outdated citations with their active sources, leading to a measurable drop in recommendations.