Generative Engine Optimization: How I Use EZY.ai to Get Your Business Cited by AI

Generative engine optimization is the work of getting your business named and cited when someone asks ChatGPT, Gemini, Claude or Perplexity who to hire, and EZY.ai is the platform I use to measure it, because you cannot optimize what you cannot see. AI answers are different for every person, every session and every engine, so guessing whether you show up is worthless. Tracking it is the whole game.

This article explains exactly how I run GEO as a managed service: what EZY.ai tracks, how I build the prompt set and knowledge base, how I read the gap report, what gets changed on and off your site, and what the monthly loop looks like. If you have read my comparison of GEO and AEO, this is the operational sequel.

One framing before we start. AI engines do not rank ten results. They recommend two to seven businesses and cite the sources that convinced them. In a market with hundreds of competitors per category, being one of those few is the biggest visibility advantage available right now, and almost nobody is working for it.

What is generative engine optimization?

GEO is optimization aimed at generative engines: large language models that answer questions in sentences and recommend specific businesses. When a homeowner in Naperville asks ChatGPT for a reliable furnace repair company, the engine runs searches, reads a set of pages, weighs which businesses are corroborated across trusted sources, and names a few. GEO is the work of becoming one of the named few, consistently, across engines.

It rests on three things: clarity (machines can extract exactly what you do and where), corroboration (other trusted sources say the same thing about you), and measurement (you know which prompts name you and which name your competitors). Most businesses have done nothing on any of the three. Google’s own guidance on AI features confirms there is no special trick; the same quality and structure signals apply, just with far fewer winners per answer.

Why do you need a platform to do GEO at all?

Here is the problem with doing GEO by hand. You ask ChatGPT a question and it names three businesses. Your competitor asks the same question from a different account and gets a different three. Gemini gives a fourth list. Perplexity a fifth. Ask again next week and the answers have moved. Any single check is an anecdote, and you cannot run a strategy on anecdotes.

GEO needs what SEO has had for twenty years: a rank tracker. A fixed set of realistic customer prompts, run on a schedule, across every major engine, with the results stored so you can see trends, share of voice against competitors, and which sources the engines are citing. That is what a platform provides, and it is why I stopped doing spot checks and built the service around EZY.ai.

What is EZY.ai and what does it track?

EZY.ai is an AI visibility platform. I use it to track, at the level of individual prompts, whether a business is mentioned and cited across the engines that matter. What it measures for each client:

I sell this as a managed service under my own brand. The platform is one part of it. The monthly optimization work is the part that moves the numbers, and the report clients receive is white-labeled as their AI Visibility Report. You get the data and the work, not a login and a manual.

Step 1: build the prompt set and the baseline

Everything starts with the prompt set. These are not keywords. They are the full questions your customers actually type into an AI assistant, and they need to be realistic, specific and local. For an HVAC company that might be “who is the best furnace repair company in Downers Grove,” “is it worth replacing a 15 year old furnace,” “emergency AC repair near Naperville that is open on weekends,” and “what should a new furnace cost in the Chicago suburbs.”

I build 30 to 100 prompts per client across three types: recommendation prompts (who should I hire), comparison prompts (what does it cost, what is better), and research prompts (how does this work, what should I know). Each gets local modifiers for the communities you actually serve. Then EZY.ai runs the full set across every engine and records the baseline: who is named, who is cited, and where you stand. For most new clients the baseline is zero mentions on recommendation prompts. That number is the starting line, and it is why measuring first matters.

Step 2: build the knowledge base, everything AI needs to know

An AI engine can only recommend what it understands, and most businesses have never told the internet, clearly and consistently, what they do. So the second step is building the knowledge base: the complete, consistent set of facts about your business that AI needs in order to recommend you with confidence.

That means every service, described the way a customer would ask for it. Every community you serve. Your credentials, years in business, guarantees, hours, pricing ranges where you can state them, what makes you different in concrete terms, and your proof: reviews, results, awards, partnerships. The answers to the questions customers ask before hiring. All of it written once, kept consistent everywhere it appears, and refreshed monthly as the business changes.

This knowledge base drives everything downstream. It becomes the content on your service and location pages, the FAQ content with schema, the llms.txt file that hands AI crawlers a clean summary of your business, and the facts I push out to the off-site sources engines trust. It is the reason the managed service is worth more than the software alone: the knowledge base is the part that has to be built and maintained by a person who understands your business and the machines reading about it.

Step 3: read the gap report

With the baseline and knowledge base in place, the gap report answers the two questions that decide the strategy. Who is being named instead of you, prompt by prompt, and which sources convinced the engine to name them.

The second question is the goldmine. When EZY.ai shows that Gemini cites a particular directory, a local news site, a review platform and a competitor’s FAQ page for a given prompt, I know exactly where presence needs to be earned. Sometimes it is a directory you are not listed in. Sometimes it is a review site where a competitor has 200 reviews and you have 12. Sometimes it is a question the competitor answers on their site and you do not. The report turns GEO from guesswork into a checklist.

I also look at the sentiment data here. If you are being mentioned but described weakly, that points to the language on your own site and in your reviews, which is fixable.

Step 4: optimize the site for extraction

The on-site work makes your content easy for machines to extract and quote. It overlaps heavily with answer engine optimization, and it is the fastest-moving part of the process:

1. Answer-first passages. Under every heading, the answer in the first sentence or two, in a form that can be lifted out of context and still make sense.
2. Question-shaped headings that match the prompts in the set. If customers ask “what should a new furnace cost,” a page should answer exactly that, under exactly that heading.
3. Entity clarity. Your business name, services, areas and facts identical across the site, the Google Business Profile and every listing. Inconsistency reads as unreliability to a machine.
4. Schema. LocalBusiness, Service, FAQPage and Article markup, so the engine does not have to guess what a page is.
5. llms.txt and crawler access. A clean summary file for AI crawlers, and a robots file that does not block GPTBot, ClaudeBot, PerplexityBot or Google-Extended. A default robots setting blocking these is the most common self-inflicted wound in this field.
6. Freshness. Dates, updated facts, current prices. Engines favor current sources for anything that changes.

Step 5: earn the citations off-site

Engines trust what they can corroborate. The off-site work earns presence in the places the gap report showed the engines reading:

Directories and platforms AI trusts. Complete, consistent listings on the review and directory sites that appear in the cited-sources data, with the same facts as the knowledge base.

Reviews with substance. A steady flow of reviews that mention specific services and neighborhoods. Engines read review text, not just star counts, and a review that says “replaced our furnace in Lisle in one day” is a citation in its own right.

Local press and community presence. Real coverage, sponsorships, association memberships. The same authority work that powers SEO, aimed at the sources engines cite.

Video. AI engines cite YouTube heavily. A short, clear video answering a prompt from the set, with a transcript, is one of the most reliable ways to earn a citation. That is why YouTube SEO is part of the service for clients who will get on camera.

Step 6: the monthly loop and the AI Visibility Report

GEO is not a project. It is a loop, and the loop runs monthly:

1. Refresh the knowledge base. New services, new areas, new proof, new answers. Push the changes everywhere they belong.
2. Re-run the prompt set across every engine in EZY.ai.
3. Compare to last month. Which prompts gained a mention, which lost one, how share of voice moved against each competitor, which new sources are being cited.
4. Act on the gaps. Content to publish, listings to fix, reviews to earn, questions to answer.
5. Report. The AI Visibility Report, with screenshots of the actual answers naming you, delivered alongside the regular monthly reporting.

Screenshots matter. An owner seeing ChatGPT recommend their business by name, in an answer to a real customer question, understands GEO instantly in a way no chart can achieve.

What results look like

The pattern I see across clients is consistent. Month one, the baseline shows zero or near-zero mentions on recommendation prompts, even for businesses that rank well on Google. Within the first two to three months of the loop, mentions appear on the research and comparison prompts where the answer-first content was built. Recommendation prompts follow as off-site corroboration and reviews accumulate, usually over months three to six. Share of voice against competitors then climbs steadily, and the trend line becomes the proof.

The businesses that move fastest are the ones that already had solid SEO and a complete Google Business Profile, because the engines already trusted them and only needed the structure. That is why I run GEO on top of the fundamentals, never instead of them. You can see documented outcomes on my results page and hear from clients directly in their reviews.

What does managed AI visibility cost?

I offer managed AI visibility at $495 per month. That includes the EZY.ai platform tracking your prompt set across every major engine, the monthly knowledge-base refresh and optimization work, the on-site and off-site changes the gap report calls for, and the white-labeled AI Visibility Report with screenshots. It is designed to sit alongside an SEO or Google Business Profile engagement, and it is month to month.

The honest comparison: the platform on its own is far cheaper, and if you have a marketer on staff who will build the prompt set, maintain the knowledge base and act on the gap report every month, you could run it yourself. The service exists because almost nobody does that consistently, and consistency is what produces the citations.

Does GEO replace SEO?

No. GEO sits on top of SEO. The engines find candidates by searching, so a site that does not rank, cannot be crawled, or has no authority does not get cited no matter how it is structured. The order is always the same: a technically sound site and a complete Google Business Profile, then answer-first structure and schema, then the measured GEO loop. I walk through the foundation in what SEO actually involves, and the profile in how I optimize a Google Business Profile completely. Do those, add this, and you are working on all of the surfaces your customers use at once. You can also browse 500+ answers to questions about every service I offer.