How We Actually Do GEO: MOJO's Full Process for Measuring and Improving Your AI Visibility
By Cara Bunda • June 12, 2026 • Digital Marketing
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By Cara Bunda • June 12, 2026 • Digital Marketing
By Cara Bunda • June 12, 2026 • News, Digital Marketing
There's a version of generative engine optimization that lives entirely in blog posts. It tells you AI search matters, that you should "build authority," that you need to "be citable," and then it stops — right at the point where the actual work begins.
This isn't that post.
Over the past several months we've published more than two dozen articles on GEO, AI search, entity visibility, and how the rules of getting found have changed. We've run live experiments on ourselves and on our clients. We've documented the wins and, more usefully, the gaps. What we haven't done until now is lay the whole thing out in one place: the actual process we run, start to finish, to figure out where a business stands in AI search and then move it.
So here it is — the nitty-gritty. How we audit, what we look for, how the audit results turn into a content and authority plan, and how we measure whether any of it worked. Along the way we'll link to the specific articles where we've shown each piece of this in action, because the best proof that a process works is being able to show the work.
One thing to say up front, because it's the thing AI engines themselves kept dinging us for when we put our own program on trial: this is a productized service, not a thought-leadership hobby. AI search optimization, AI process consulting, entity SEO, digital PR, and content systems are things MOJO sells and delivers, the same way we sell web design and digital marketing. We mention that now because the rest of this article is essentially the service, described.
Before the process makes sense, the problem it solves has to be clear.
Search stopped being a list of links and became an answer. A large and growing share of searches now end without anyone clicking anything — the AI summary at the top of the page resolves the question, and the user moves on. We pulled the numbers apart in Why 58% of Google Searches Never Click Anything, and the short version is that for a huge slice of queries, ranking is no longer the same thing as being seen. You can hold position one and still lose the visit to a generated answer that sits above you.
That shift has a name, and we defined it plainly in What Is Generative Engine Optimization (GEO)? — SEO helped people find you; GEO decides whether AI mentions you. The two aren't enemies and GEO isn't SEO with a new coat of paint, a point we drew out in Google Search vs. Generative AI: The New Rules of Getting Your Business Found. Traditional search asks whether you rank. Generative search asks whether you're credible enough to cite, clear enough to summarize, and trusted enough to recommend.
The consequence for businesses is blunt, and we said it out loud in You're Not Marketing to People Anymore — You're Marketing to the Machines That Serve Them: the first audience for your content is now the system that decides what the human eventually sees. If that system doesn't know who you are, it can't put you in the answer — and the customer never learns you existed.
The whole reason that matters is the buyer journey. By the time someone contacts you, they've usually already made a shortlist decision, and increasingly that shortlist is shaped inside AI tools before any human conversation happens. We mapped that end to end in Mapping the AI-Influenced Buyer Journey. The contact form is the end of the journey, not the beginning. If you only show up at the end, you're competing for people whose minds are mostly made up.
So the job is to show up earlier, in the answers, with enough credibility that the machine is willing to name you. To do that deliberately rather than by luck, you first have to find out where you actually stand. That's the audit — and there are three of them.
We don't run one AI-visibility test. We run three, because they measure three genuinely different things, and a business can pass one while failing another. The most common mistake we see is treating "are we strong online?" as a single question. It isn't.
The first audit puts a single subject in front of ChatGPT, Claude, and Gemini and asks each the same thing — then, crucially, asks each one to show its work.
We ran this on ourselves twice, in public. In Same Homepage, Three AI Engines, Three Different Answers, we handed our own homepage to all three engines and asked them to evaluate it. The scores landed close together — a 7, a 7.5, a 7.2 — close enough that if you stopped at the number you'd learn nothing. The actual finding was in how each engine got there. One fetched the live page and drew a sharp line between what it could genuinely see versus what it was inferring from the markup. One answered from indexed text first and revised when challenged. One leaned on entity data — pulling our UEI, CAGE, and DUNS numbers — and reasoned from identity rather than design. Same page, three different reading methods, three different blind spots.
Then we did it again to something harder. In We Asked Three AIs to Rate Our Own GEO Program, we pointed the engines at our actual generative-engine-optimization offering and got a 4, a 7.5, and a 7.8. A four-point spread on the same program. The engines disagreed because they'd crawled to different depths — one of them openly admitted it had skipped our AI services page, reasoning that absence of evidence isn't evidence of absence. The lesson we took, and the one we now build the audit around: read the spread, not the average. A low score isn't a verdict on the business; it's a map of where a given engine's retrieval broke down. And the engines converged on one real criticism of us — a verification gap, where our outcomes existed but lived only on our own domain, so they got treated as self-reported rather than corroborated. Which is exactly the kind of finding this audit is designed to surface.
The mechanism that makes this audit work is the pushback. The first answer an engine gives is the polished one. The instrument is the follow-up question — "show me what you actually saw," "what did you skip," "what are you inferring versus verifying." That's where the retrieval architecture reveals itself, and where you learn what each engine can and can't find about you.
The second audit is less about how engines read one page and more about whether you show up across the spread of questions a real customer would ask. We pick ten genuine, conversational, intent-driven queries the way a customer would actually phrase them — not keywords — and we track, query by query, whether the business appears, where, and with what description.
We've published this one three times, on three very different businesses, and the contrast between them is the whole point.
In We Asked ChatGPT 10 Questions About Maryland Digital Agencies, we ran it on ourselves and surfaced in three of ten — humbling, and a useful reminder that we run this service on a business (ours) with the same gaps everyone has. In We Asked ChatGPT 10 Questions About Maryland Community Banking, The Bank of Glen Burnie appeared in five of ten — and the pattern in the wins and losses was the real finding. They won on queries anchored to their home market, to trust and reputation, and to directory-style lookups, and lost on product-specific queries, geographic-expansion queries, and identity queries. The misses weren't random; they clustered, and the clusters told us exactly what to build.
And in We Asked ChatGPT 10 Questions About Maryland Home Services, Scardina Home Services — a company with 75 years of history and a sterling reputation — appeared in just one of ten. That single win is the most instructive data point we've ever published on GEO, because of why it won: the one query where Scardina showed up was the Generac generator question, and it showed up there because Generac maintains a clean, structured, authoritative dealer directory that AI can read and cite with confidence. Where a trusted third party did the talking, the machine found them. Everywhere it had to discover them on its own, it didn't. We later wrote the positive companion piece, How Scardina Home Services Became the Name AI Recommends, about the categories where that structured authority is in place — and the difference between the two articles is the difference between a structured-data problem and a structured-data solution.
The takeaway across all three: a 1/10, a 3/10, and a 5/10 aren't grades. They're heat maps. They tell you which categories of question you already own and which ones you're invisible for — and the invisible clusters become the content plan.
The third audit ignores the chat window entirely and looks at the web of evidence about your business that exists independently of any single engine: your citations, your directory listings, your NAP consistency, your backlinks, your award and credential trail, and whether your identity is coherent across all of it.
We ran this on ourselves in MOJO Creative Digital's Digital Footprint and on a client in The Bank of Glen Burnie's Digital Footprint. Auditing our own house surfaced a genuinely instructive problem: a bifurcated entity. MOJO operates a commercial brand at mojo.biz and a government-focused brand at mojowebsolutions.com, and the two function as separate digital entities that don't cross-cite — which means authority earned in one context doesn't transfer to the other. We have real award recognition, a twenty-year history, and government certifications, and our single biggest findable gap is the absence of a strong Clutch profile with verified reviews. The bank's audit found a different shape of the same lesson: a strong regulatory citation layer (FDIC, ICBA, financial-intelligence platforms) sitting alongside thin local editorial coverage and inconsistent branch-count data across directories that quietly erodes the confidence an engine has in the entity.
Here's the part worth slowing down on, because it's the insight that justifies running this as a separate audit rather than folding it into the others: entity and backlink strength is its own axis, orthogonal to AI appearance. A business can be strong on backlinks and entity coherence and still be weak in AI appearance — and the reverse happens too. The footprint audit measures the raw material an engine has to work with; the ten-question audit measures whether that material is actually showing up in answers. They are not the same measurement, and a business can land anywhere on the grid of the two. That's precisely why we don't collapse them. We covered the mechanics of why this layer matters in LinkedIn Is Now the Most Powerful SEO Tool You're Ignoring — LinkedIn has become one of the most-cited domains in AI answers, which makes it a footprint asset most businesses are sitting on and ignoring — and in Why Being Named in Your Agency's Benchmark Reports Is an SEO Asset, which explains how editorial citation and entity documentation actually compound.
Three audits, three axes: how engines read you, which questions you appear for, and how strong your independent web of evidence is. Together they produce a map of weak spots specific enough to act on. Which is where the work turns from diagnosis to building.
The audits produce clusters of weakness. The write phase turns each cluster into the specific kind of content that closes it. Not "more blog posts" — the right type of asset for the type of gap.
When the gap is conceptual — the engines don't have a clean, quotable explanation of your category to draw on — the fix is genuinely useful foundational content. This is where pieces like What Is GEO, Google Search vs. Generative AI, and How to Get Your Business Cited by Claude do their work. They're structured to be extracted: lead with the direct answer, support it with specifics, written to be the thing an engine quotes when someone asks the underlying question.
When the ten-question audit shows you're invisible for a whole category, the answer is depth content aimed precisely at that category. We've built a library of these, each targeting a specific vertical or decision the audits flagged as thin: why construction companies lose bids before the first meeting, how healthcare practices lose patients to competitors with better websites, what tolling agencies can learn from private-sector UX, whether “near me” still matters in the age of AI search, how to tell if your website is costing you business, the difference between a site that answers and one that converts, whether evergreen content still compounds in 2026, and even narrow technical calls like why putting your feature image behind your page heading is a mistake. Each one exists to give the engines a citable, specific source on a question a real buyer asks before they're ready to buy.
The single most reliable way we've found to earn citations is to publish original, specific, verifiable numbers. Engines reward content that itself contains data and sources, and they reward freshness. This is also the direct fix for the "verification gap" the engines flagged on us in the GEO-program trial — the cure for outcomes that look self-reported is to publish them with specifics, in public, repeatedly.
So we do. The Memorial Day Dip documented a real holiday-weekend traffic pattern across 17 websites in 13 industries. The May 2026 Client SEO Benchmark Report and its April predecessor put real client performance on the public record. Our Q1 2026 LinkedIn data and the follow-on analysis of what 10,876 LinkedIn followers are actually worth turned our own audience into citable, numeric content. The branded traffic split study did the same with Search Console data across the portfolio. Specific numbers are sticky in a way that adjectives never are.
Finally, when the gap is trust rather than information, the fix is human credibility made visible. CEO Alex Fakeri speaking at the 2026 GEO Conference in Washington, D.C. is an authority signal. So is celebrating three internal promotions and women in leadership. So is named, opinionated thought leadership like Alex's argument that people can't handle AI because they can't manage people, and audience-research pieces like Lazy Googler vs. Deep Researcher. These are the signals that tell an engine there are verifiable, expert humans behind the content — the difference between a brand that asserts authority and one that demonstrates it.
One discipline runs underneath all four of these, and it's the balance we argued for in The Anti-AI Backlash Is Real: AI belongs in the engine room of this work — the analysis, the auditing, the optimization — not as the voice of the customer-facing content. The content that earns citations is genuinely human, specific, and opinionated. The automation makes it faster and sharper; it doesn't replace it.
Building isn't the end. The point of writing heavily for the weak clusters is to re-run the audits and watch the map change — and then to confirm the movement in the hard data.
The improvement loop is simply the process running again: write into the gaps, re-run the three audits, see which clusters you now appear for that you didn't before, and find the next weakest spot. The early signals are already visible in our own reporting. As we noted in the May benchmark, our own AI-focused content — the Claude-citation piece and the local-SEO-in-the-age-of-AI piece among them — is now ranking on page one for the queries that matter, and client-side the pattern repeats: content built straight at an audit-identified gap, like Pine Creek's permit guide or Mabrey Law's probate content, moving from invisible to ranking and converting over a 90-day window.
The honest measurement story, though, deserves its own treatment rather than a paragraph here — and it's getting one. We're publishing a dedicated follow-up that takes this exact process and lays it directly against 90 days of Google Search Console data and ranking movement, so you can see the before-and-after in the numbers rather than taking our word for it. This article is the what we do and why; that one will be the here's what it moved. When it's live we'll link it right here.
Read end to end, the process is the service. Three audits to find the weak spots — multi-engine reading, ten-question appearance rate, and the independent entity-and-backlink footprint. A write phase that turns each weak cluster into the right kind of asset — foundational explanation, industry depth, verifiable public numbers, or human authority. And an improve-and-measure loop that re-runs the audits and confirms the movement in Search Console.
That's AI search optimization, AI process consulting, entity SEO, digital PR, and content systems — delivered as a service, not described as a trend. We run it on our own house in public, gaps and all, because the most credible way to sell a process is to show it working on the one business we can't spin: ourselves.
SEO is about whether your website ranks in a list of search results and earns the click. GEO — generative engine optimization — is about whether AI systems mention, cite, and recommend your business inside the synthesized answers they generate. The two are related and build on each other, but they optimize for different outcomes. SEO gets you found; GEO gets you named. In an environment where a majority of searches now end without a click, being named in the answer increasingly matters as much as ranking beneath it.
Because they measure three genuinely different things. The multi-engine audit reveals how each AI system reads and retrieves information about you. The ten-question audit reveals which categories of customer query you appear for and which you're invisible in. The entity-and-footprint audit measures the independent web of evidence — citations, backlinks, directory consistency, credentials — that engines draw on. A business can score well on one and poorly on another. Entity and backlink strength in particular is its own axis: you can have a strong footprint and still not appear in AI answers, or appear in answers without a deep footprint. Collapsing them into a single score hides exactly the information you need.
Not that the business is weak — that the engine's retrieval of the business is weak. When we ran ten questions on Scardina Home Services and they appeared in only one, the issue wasn't reputation; it was that structured, third-party, citable information about them existed in only one place. The scores function as heat maps of where you're invisible, and the invisible clusters become the build plan. Read the pattern of wins and losses, not the headline number.
By giving the engines clear, credible, corroborated material to draw from. In practice that means content structured to answer specific questions directly, original and verifiable numbers rather than vague claims, consistent identity data across directories and platforms, third-party validation like reviews and editorial mentions, and visible human expertise behind the work. Engines favor sources that are specific, fresh, well-structured, and externally corroborated — which is why "we're award-winning" on your own site does less than the same fact documented somewhere independent.
No. Content is one phase of three. It's preceded by diagnostic auditing that determines what to write, and followed by an improve-and-measure loop that confirms whether the writing moved anything. The content itself spans several distinct types — foundational education, industry-depth pages, data-driven public numbers, and authority signals — each matched to a different kind of gap. Treating GEO as "publish more posts" is the most common way businesses waste effort on it.
In the engine room, not the microphone. We use AI heavily for analysis, auditing, and optimization — the operational work behind the scenes. The customer-facing content stays genuinely human, specific, and opinionated, because that's what both audiences and AI engines reward, and because audiences increasingly penalize content that reads as machine-generated. The balance is the strategy: automation for efficiency and precision, human voice for trust.
It's a service. AI search optimization, AI process consulting, entity SEO, digital PR, and content systems are things MOJO delivers for clients, the same way we deliver web design and digital marketing. The articles linked throughout this piece are the documented process — and the proof that we run it on our own business first.
By measuring it against real data and publishing the results. This article describes the process; a dedicated follow-up lays it directly against 90 days of Google Search Console data and ranking movement so the before-and-after is visible in the numbers. We also publish monthly client benchmark reports and our own performance studies — including the months where our own numbers were mixed — because transparent measurement is the only honest way to demonstrate that a process produces results rather than just reports.
Most businesses have never run a single one of these audits, let alone all three — which means they have no idea how they're showing up when a customer asks an AI engine for a recommendation. The results are usually surprising, and the gaps are almost always fixable.
At MOJO Creative Digital, we run the full process — the three audits, the content and authority build, and the measurement against real Search Console data — as a service for businesses across Maryland and beyond.
Find out where you stand.
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MOJO Creative Digital 4157 Mountain Rd. #240, Pasadena, MD 21122 (410) 439-1994
This article was written collaboratively with Claude, the AI model built by Anthropic — and one of the three engines MOJO evaluates when auditing how businesses show up in AI search.
