Improve your chances of being cited by ChatGPT, Gemini, Perplexity, and Claude; not skipped.
When buyers ask an AI who to hire, the brands that get named win the deal before competitors know it happened. GEO makes your business the source generative engines pull from and cite, by engineering the entity data, content structure, and authority signals that earn you a seat in the answer itself.
Generative Engine Optimization (GEO) is structuring your content, entities, and authority so AI answer engines like ChatGPT, Gemini, Perplexity, and Google AI Overviews are more likely to retrieve, trust, and cite you organically, without paying for placement (inclusion is never guaranteed).
We map the exact questions your buyers ask an assistant, then see what each engine retrieves and cites for those questions today, and close the gaps with answer-first, citation-ready content, clean structured data, and the third-party signals models trust, so you become a source the engine reaches for. (Our Persona Engineering service produces the buyer-question map this runs on.)
Any business whose buyers research in AI first, whether the goal is revenue, qualified leads, inbound phone calls to a local or service business, or simply being the brand an assistant names when a prospect asks who to use. It is the organic counterpart to GEM (paid).
A service company invisible in ChatGPT has us map its buyers' questions through Persona Engineering, then publish answer-first pages and earn citations; weeks later the assistant names and links it when buyers ask for a recommendation, driving calls with no ad spend.
See if Generative Engine Optimization (GEO) is the right move for your team.
Request a free quoteGenerative Engine Optimization gets your brand surfaced inside AI answers from tools like Claude, ChatGPT, and other assistants where buyers now research. Our senior team builds the content, structure, and entity signals that make your business citable, then we run it as an ongoing program. This is hands-on work tied to revenue, not a one-time report on where you rank.
We monitor how AI assistants answer questions about your category and adjust the program as the engines and your competitors move.
Illustrative example, styled to show the kind of output we deliver.
Getting named and cited inside AI answers, the new front page of search.
Result Started getting named and cited across several category queries.
Result Earned AI Overview citations on bottom-funnel queries.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
Generative engines don't rank ten links, they retrieve a handful of passages, check whether the facts corroborate across sources they trust, and cite the ones that answer cleanly. GEO works by shaping your content, entity data, and off-site authority so your brand is the passage that survives that retrieval-and-corroboration step, on the specific questions buyers actually ask.
Traditional SEO optimizes for a ranked list a human scans; generative engines instead synthesize one answer from a few retrieved passages and prefer facts that are consistent, well-structured, and corroborated across trusted sources. That's why GEO compounds: every accurate entity signal and independent citation you add makes the next retrieval more likely to surface and trust you, while a competitor relying on one-off tweaks is far less likely to be cited consistently. You can't force a citation, but you can systematically make your brand the lowest-risk, easiest-to-quote source, which is what the model is optimizing to find.
SEO optimizes to rank a blue link on a results page, while GEO optimizes to be the source an AI assistant quotes inside its answer. They overlap on technical health and authority, but GEO adds work like structuring content so models can extract it cleanly, earning mentions on the third-party sites these tools synthesize from, and tracking whether you get cited rather than where you rank. We usually run GEO alongside your SEO so the two reinforce each other instead of competing for the same content.
We build a prompt set of the real questions your buyers ask and run them across each assistant on a recurring schedule, then log whether you are named, linked, or skipped, and which competitor shows up instead. We track share of citations by topic and by engine over time, because each model pulls from different sources and updates on its own cadence. For example, Perplexity tends to cite live web pages directly while ChatGPT leans on its training plus connected search, so the same answer can name you in one and ignore you in another, and we tune for each.
On-site we restructure content into clear, extractable answers with strong headings, direct definitions, FAQs, and schema so models can lift a clean passage. Off-site we work the sources these engines trust, which means earning mentions and accurate listings on the review sites, directories, and editorial pages that get synthesized into answers. We also fix the boring blockers, like making sure your robots and crawl rules are not quietly excluding the AI crawlers that feed these tools.
Content and technical changes can surface in crawl-based tools like Perplexity within a few weeks, while models that update less often can take a couple of months to reflect new authority, so realistic movement shows over roughly two to four months. We do not guarantee specific placements, because no agency controls how these models rank or phrase answers. What we commit to is the work and the reporting: tracking citations across engines, executing the optimizations, and reporting the trend every cycle, with more citations across more of your priority prompts as the target we drive toward.
You do not need volume, you need clear, authoritative coverage of the specific questions buyers ask, so a focused set of strong pages often outperforms a large thin one. We start by mining the prompts your audience actually uses and prioritize the handful of topics where being the cited answer matters most to revenue. For example, a niche B2B firm with twenty sharp, well-structured pages can become the go-to source on its category, while a sprawling site with shallow content gets passed over by the model.