AI Search Optimization (GEO) Services

Generative engine optimization (GEO for short, closely related to what some in the industry call AEO services, or answer engine optimization) is the practice of making your content easy for AI systems like ChatGPT, Perplexity, Google's AI Overviews, and Google's Gemini to find, understand, and cite when they generate an answer.

It's a close cousin of SEO, not a replacement for it.

The same content and authority signals that help you rank still matter, but generative engine optimization services also account for how large language models parse, summarize, and attribute information, which is a different mechanical problem than ranking a blue link.

This is worth taking seriously now, not eventually. Google AI Overviews already appear in roughly 47% of search results, meaning a real share of searches never produce a traditional list of ten links at all. The AI-generated summary is the only thing many users see.

The market reflects that shift: generative engine optimization is estimated at around $1.23 billion in 2025 and projected to grow toward $22 billion by 2034 as more search behavior moves through AI interfaces.

If you're newer to the concept, our primer on what generative engine optimization actually is walks through the mechanics in more depth before you get into buying decisions.

We'll be direct about where this space actually stands: GEO in 2026 does not have a standardized measurement methodology the way SEO has Search Console and consistent rank tracking.

Anyone selling you a proprietary "GEO score" as an industry-standard metric is selling you something that doesn't fully exist yet. We treat it as an emerging discipline worth investing in early, not a solved science with guaranteed numbers attached.

In practice, ai search optimization services means structuring content so an AI model can lift it cleanly and attribute it correctly.

That includes FAQ schema and other structured data markup that gives AI crawlers an explicit, machine-readable version of your content instead of forcing them to infer it from prose.

It includes clear authorship and expertise signals on service and resource pages, since AI systems, like search engines, weigh who appears to be behind a piece of content.

It includes presence on the third-party sources AI models pull from most often, which one widely cited large-scale study of AI citations found skews heavily toward Wikipedia across ChatGPT, Perplexity, and Google AI Overviews, with Reddit especially prominent in AI Overviews specifically and YouTube performing strongly inside Perplexity and AI Overviews results.

And it includes content formatting: clear headings that map to actual questions, direct answers placed near the top of a section rather than buried under three paragraphs of preamble, and structured lists and tables where they genuinely aid comprehension. That's the format LLMs tend to extract most cleanly, not one we're recommending as a trick.

We start by auditing what's already citable on your site: structured data present or missing, how your existing content is formatted, and whether your service pages currently give an AI model enough clear signal to summarize accurately.

From there we prioritize the pages most likely to get cited or surfaced: comparison content, definitional and glossary pages, and service pages answering a specific buyer question tend to perform best in generative results, more so than generic homepage copy.

We implement schema markup (FAQ, HowTo, Organization, and Article schema depending on the page), restructure content for direct-answer clarity, and build out citation presence where it's realistic for the business, from review platforms and relevant directories to third-party mentions that AI systems can point back to.

This is also where we work on the practical question most clients actually ask: how to rank in ChatGPT and similar tools, which comes down less to a single trick and more to being the kind of clearly structured, well-attributed source these models already prefer to pull from.

We also track what we can track honestly: which of your pages get surfaced in AI Overviews and answer engines where visibility tools allow monitoring, alongside the traditional organic signals that still correlate with AI visibility. We don't report a single composite score, because as of today that number wouldn't mean anything standardized.

GEO isn't a separate marketing channel competing with our SEO services for budget. It's mostly the same underlying work, aimed at an additional set of outputs. A page with solid technical SEO, clear structure, and genuine expertise behind it is already most of the way to being AI-citable.

The incremental GEO work is schema, formatting, and citation-building layered on top of an SEO foundation that has to exist first. Businesses skipping SEO to chase GEO directly are usually skipping the part that makes GEO work at all.

At this stage of the market, we don't sell GEO as a fully separate retainer line the way we would PPC or content production.

It's typically bundled into an SEO engagement, priced within the same $500–$5,000/month range depending on scope, with GEO-specific deliverables (schema, structured content formatting, citation outreach) built into the plan rather than billed as an add-on.

This is partly a pricing philosophy and partly an honest reflection of where the category is: GEO as a fully standalone, independently priced service isn't a mature enough market yet for us to sell it that way without overpromising.

Independent research from firms tracking AI citation patterns (Ahrefs, for example, publishes ongoing analysis of which sources large language models cite) is still evolving quickly, and any specific figures from studies like these should be checked against current data at the time you're reading this rather than treated as fixed.