When an assistant answers instead of listing links, there is no page two to climb back from. Two or three businesses get named and the rest of the market is not in the conversation at all. Agentic Visibility Optimization is the work of being one of the names.
This is the part almost nobody explains, and it is the whole reason the old scoreboard stopped working. When someone asks an assistant for a recommendation, the machine does not run their question. It writes its own.
That last point is the uncomfortable one. You cannot lose a customer in a search result without at least appearing in the data. You can lose one here and have absolutely no record that it happened.
| The term | What it actually means | Our view |
|---|---|---|
| SEO | Being found when someone searches. Still 90% of the volume and still where almost all the money is. | The foundation. Nothing else works without it. |
| AEO Answer engine optimization |
Being the source a machine quotes when it answers a direct question. | A slice of the same job, not a service. |
| GEO Generative engine optimization |
Being present in text a model generates rather than in a list of links. | A slice of the same job, not a service. |
| AVO Agentic visibility optimization |
Being retrieved, reasoned about and chosen by the agent doing the work, whichever surface it appears on. | The broadest of the four, and the one that names the real target. |
We use AVO because it describes the target honestly: the agent, not the answer box it happens to print into. But we are not going to pretend a new acronym is a new industry. If you are being quoted separately for SEO, then GEO, then AEO, you are paying three times for one discipline. The work below is what any of those words should mean.
Data becomes knowledge, knowledge becomes trust, trust is what gets you named. Skipping to the top of that stack is how people spend a year on AI visibility and move nothing.
Every machine that might mention you needs to agree on what is true about you. One address, one phone number, one set of hours, one list of what you actually do, consistent everywhere it appears.
This is where schema markup belongs. It is worth doing and we do it, because it makes your facts unambiguous and it helps conventional search. What we will not do is sell it to you as the thing that gets you into AI answers, because that specific claim has been tested and did not hold up.
Facts make you correct. Framing makes you the answer. A model choosing between four licensed roofers is looking for a reason to name one, and that reason lives in how clearly your work, your speciality and your difference are written down.
In practice: pages that answer real questions in plain language, comparisons that are honest about who you are not right for, and the difference stated somewhere other than a slogan.
These systems do not read your website live in most cases. They read what they absorbed, and what they can retrieve from sources they already trust: business listings, review platforms, forums, news, third-party mentions.
So the work is getting accurate, consistent information about you into the places these systems actually draw from. Not your site alone. Your site is the easiest input to control and the least persuasive one.
Reviews with substance in them. Real numbers from real jobs. Comparisons. A named human behind the business. Coverage that is not on a domain you own.
A machine deciding whether to vouch for you is doing roughly what a cautious customer does, and it reaches for the same evidence. Thin proof reads as thin to both.
We keep a list of the questions your customers would actually ask, run them on a schedule across ChatGPT, Google's AI answers and Perplexity, and record three things: whether you were named, what was said about you, and who got named instead.
It is reported as a trend with the variation visible, never as a single score. Running an identical prompt three times returns the same set of citations only about 2% of the time, so a number that moved from 4 to 6 this month may mean nothing at all. A report that hides that is a report designed to be renewed, not to be true.
Here is the whole argument in one click. These open an assistant with a question already written, asking it to look us up and tell you honestly whether we are worth your time. If we had not done this work on ourselves, you would get a vague answer, and you would know exactly what that means.
We do not see the conversation, and we do not get to edit the answer.
That sequence, asking a model to look something up and then reason about it out loud, is not a gimmick. It is a compressed version of Layer 2 and Layer 3, and it is the fastest way to find out what these systems currently believe about any business, including yours.
Every figure on this page comes from a published study rather than from our own results. Where we are inferring rather than measuring, we say so. If a number here is ever shown to be wrong, we would rather change it than defend it.
AI assistants are still well under one percent of total search volume. They are also growing several hundred percent a year while conventional search grows in the teens. Both of those are true, and an agency that only tells you the second one is selling you panic.
Which is why we do not sell this as a separate retainer to most people. AI search tracking is included in both of our local SEO plans, because the work that feeds it is the work we were doing anyway.
Not really, and that is the honest answer. GEO stands for generative engine optimization and AEO for answer engine optimization. Both describe a slice of the same job: being present and trusted when a machine writes the answer instead of listing links. We use Agentic Visibility Optimization because it is the broadest of the three and it names the actual target, which is the agent doing the reasoning. If someone quotes you separately for SEO, GEO and AEO, you are being charged three times for one discipline.
No, and nobody can. Around three quarters of the sources ChatGPT cites change from one week to the next, and running the same prompt three times returns a stable set of citations only about 2% of the time. Anyone selling a fixed position in an AI answer is selling something they do not control. What is honest is measurement over time and doing the work that demonstrably feeds these systems.
Not on its own. A test of 1,885 pages that added JSON-LD against roughly 4,000 that did not found the effect on AI citations indistinguishable from noise. Schema is still worth doing, because it helps conventional search and it keeps your facts machine readable and unambiguous. The problem is agencies selling schema as the headline AI deliverable when the measured evidence does not support that.
Not urgently, and we would rather say so. AI assistants are still well under one percent of total search volume. They are also growing several hundred percent a year while conventional search grows in the teens. The sane position for a local trade is to get the fundamentals right, which feed both, and to measure AI visibility monthly so you see the shift coming rather than reading about it after it has happened.
By polling, not by rank tracking. We keep a set of real questions your customers would ask, run them on a schedule across ChatGPT, Google AI answers and Perplexity, and record whether you were named, what was said about you, and who was named instead. Because the answers vary between identical runs, it is reported as a trend with the noise shown, never as a single ranking number.
We run your business through ChatGPT, Google's AI answers and Perplexity, and send you what came back, including who got named instead of you. No charge, and no obligation to do anything about it. The profile and review signals those engines actually read are listed out in our free checklist for getting named in AI answers. If the site itself is what is holding you back, AI-ready from the Lightspeed build is where most of our clients start.