AI search optimization that gets your business named and cited.

An AI answer is assembled, not recalled

Most owners picture the model as an encyclopedia that either knows about them or doesn’t. For local questions, that’s not how it works. Ask ChatGPT for a roofer in Arlington and it searches, opens a few pages, and synthesizes a response on the spot. The model supplies the writing. The sources supply the facts about you.

That distinction is the whole job. You are not trying to be memorized — you’re trying to be retrievable and quotable at the moment the question gets asked. It also means the answer isn’t fixed. Two people asking the same thing an hour apart can get different companies named, because the retrieval ran again. Anyone selling you a permanent “spot” inside an AI answer is selling something that does not exist.

The shortlist is shorter than page one

A Google results page hands the searcher ten options plus a Map Pack, and position five still earns clicks — plenty of good businesses have been built out of fourth place. An AI answer usually names two or three companies. Sometimes one. There is no scrolling past the fold, no second page, no “see more results.”

That’s the risk and the opening in the same breath. The bar for inclusion is higher than it ever was on a results page — and almost nobody in your market is deliberately working on it yet. The businesses that get named in 2027 are the ones putting the foundations in now, the same way the businesses that owned the Map Pack were the ones who took it seriously before their competitors did.

Most of what an engine reads about you isn’t your website

When a model assembles a local recommendation, your site is one input among many, and rarely the loudest. It is also reading your Google Business Profile, the actual language inside your reviews, the directories and trade associations that list you, local news mentions, and forum threads where somebody asked this exact question two years ago.

This is why AEO isn’t a content trick you bolt onto a homepage. If your listings disagree about your phone number, if your reviews never happen to mention the service you most want to be known for, if you’re missing from the two or three sources that keep getting cited in your trade — the engine has nothing consistent to say about you. So it says nothing, and names somebody else.

Being recommended starts with being resolvable

Before an engine can vouch for you, it has to be confident you are one real, specific business. That is an identity problem, not a keyword problem. Organization and LocalBusiness schema, a name, address and phone that match everywhere without exception, and sameAs links tying your site to your verified profiles are what let a machine collapse a few dozen scattered mentions into a single confident record.

Get it wrong and you can be genuinely well known in your town and still invisible here. Two spellings of your business name across the web don’t read as one strong company — they read as two half-credible ones, and a system built to avoid being wrong will quietly skip both. Fixing that is unglamorous work: citation cleanup, schema, profile verification. It is also the step everything else depends on.

Write the answer, not the run-up

The pages that get quoted lead with the answer. The question as a heading, a direct two-sentence response underneath it, then the depth for the humans who want it. “How much does water heater replacement cost in Fort Worth?” followed immediately by a real number beats four paragraphs of company background that eventually gets there.

That’s not a compromise you make for the machines. It’s how people wanted to read all along — AI search just made the preference expensive to ignore. The upside is that the same rewrite tends to lift your conversion rate, because a visitor who gets their answer in the first ten seconds is far more likely to still be on the page when the phone number shows up.

Your reviews are the adjectives

When an AI describes your company, the descriptive language usually comes from your reviews. Not the star rating — the words. A business with forty reviews that repeatedly say “showed up same day” and “explained the cost before starting” hands an engine something specific to recommend you for. A business with the same rating and forty reviews that only say “great job, thanks!” hands it nothing.

So review generation stops being a vanity metric and becomes source material. We build the ask into the end of the job and prompt for the specifics — the service, the town, the thing you did well — without ever scripting a customer’s words for them, which violates Google’s rules and reads as fake to a model anyway.

What we measure, and what we won’t promise

There is no rank tracker for AI answers, because there are no ranks — there’s a generated response that shifts with phrasing, location and date. So we measure it the honest way: a fixed set of prompts a real customer would actually type, run on a schedule across ChatGPT, Gemini, Claude, Perplexity and Google’s AI Overviews, and logged over time. You see when you start getting named, how often, and whether what’s being said about you is even accurate — which, before this work, it frequently isn’t.

What we won’t do is promise placement. Nobody can buy their way into a model’s answer, and an agency that guarantees you a spot is either guessing or lying. What we will do is make you the most legible, best-documented, easiest-to-recommend business in your market — and we ran the playbook on our own site first, entity graph, structured answers and an llms.txt for the crawlers included. From there the engines make their own call. That’s how it should work, and it’s why the businesses that do this properly are hard to dislodge once they’re in.