Generative Engine Optimization

A GEO agency engineers the signals answer engines weigh when choosing sources: content that answers first and carries citable statistics and quotations, entity data that agrees everywhere machines check, structured data, deliberate AI-crawler access, and third-party coverage on the sites engines retrieve. Then it tracks which prompts cite your brand each month, per engine, against a recorded baseline.

Where an engine leans on live retrieval, movement can show within weeks of a page being recrawled: Google AI Overviews and Perplexity both work this way. Mentions that depend on what a model remembers from training move on the provider’s release schedule, sometimes months. We baseline your citations in month one so progress is measured against a record, not a feeling.

No. The engines share most of their inputs: crawlable pages, consistent entity data, sourced content, and third-party coverage feed all of them. What differs is weighting: AI Overviews lean on ranking pages, chatbots lean on well-covered entities. One program builds the shared signals, and the monthly report breaks citations out per engine so the differences stay visible.

llms.txt is a plain-text file at your site root that states who you are, what you do, and where your key pages live, written for AI systems rather than people. It is an emerging convention, not a ranking lever: adoption by engines is uneven. We ship one because it costs little, states your entity in your own words, and cannot hurt.

It is a business decision, not a default. Blocking GPTBot, Google-Extended, or PerplexityBot keeps your content out of the systems your buyers increasingly ask, which is a real cost; allowing them trades content for visibility. Publishers with paywalled work often block; service businesses usually should not. We map each crawler and make the choice deliberately with you.

Rarely well. Engines retrieve sources they can find, verify, and trust; a site with no rankings, reviews, or coverage gives them nothing to retrieve. AI Overviews in particular cite pages that already rank. If the audit shows your base is the gap, we will say so and route you to foundation work first, because GEO compounds a presence rather than substituting for one.

A fixed panel of buyer prompts runs on a schedule across ChatGPT, Perplexity, Google AI Overviews, and Gemini. The monthly report records which answers cited you, which cited competitors, what each answer used as sources, and movement against your baseline. Because answers vary by session and phrasing, we report patterns across repeated runs, never a single flattering screenshot.

Schema does not buy citations, and anyone claiming a markup trick does is overselling. What it does: give machines an unambiguous statement of who you are, what you offer, and what each page answers, which supports both retrieval and entity verification. The rule we hold to is that markup must match the visible page word for word, or it works against you.

The 2023 paper that named the field, by Aggarwal and co-authors from Princeton, Georgia Tech, and other labs, tested which content changes made a source more visible in generated answers. Adding citations, quotations, and statistics lifted visibility by roughly 30-40% in their benchmark. Keyword stuffing did not help. We treat those findings as direction, not as a promise of results.

Both parts exist. The entity reconciliation, crawler access, and schema work are heavy once, then maintained. Content, source coverage, and tracking are ongoing, because engines refresh, competitors publish, and models retrain. Programs run month to month with no long contract; if the citations hold without us, the honest move is scaling the retainer down, and we say so.