Most GEO advice is about getting named in AI answers for category questions, like "best mortgage broker in Geelong". This playbook is about the other half of the problem, the half that finds you whether you invest or not: what AI says when a buyer asks about your business by name.
"Is [your company] any good?" "What does [your company] charge?" "Reviews of [your company]."
Those questions are being asked right now, and the engines are answering with whatever public record exists: accurate or not, current or not, flattering or not.
The good news: because engines assemble those answers from retrievable sources, you have more influence over your AI reputation than over almost any other channel.
You can't edit the answer, but you can edit most of what the answer is built from. Here's the playbook.
Step one: find out what AI already says about you
Before fixing anything, get the unvarnished picture. Ask ChatGPT, Gemini and Perplexity a structured set of brand questions: who is [business], what do they do and where, what do they charge, are they reputable, and how do they compare to alternatives.
Do it logged out or in a fresh session where you can, so your own history doesn't soften the answers, and save everything: the wording, the sources cited, the date.
Most businesses doing this for the first time find a mix: mostly right, a few facts years stale, and occasionally something flatly wrong, like a closed office still listed as headquarters or a service you exited in 2023 described in present tense.
Where wrong answers come from
AI engines don't invent facts about small businesses out of malice; they repeat the public record, weighted toward sources that look authoritative.
Wrong answers almost always trace to one of four places: your own website saying outdated things with confidence; third-party listings and directories carrying old addresses, phone numbers or service lists; stale coverage (an old news article, a defunct partner page) outranking anything current; or a vacuum, where you've published so little that the engine leans on whatever thin scraps exist. That last case is the most common and the most fixable: vague brands get vague answers.
Correct facts at the source, not in the mirror
You can't argue with an AI answer, but you can outrank its inputs. Work in this order:
- Your own site first: publish a plainly factual about page and keep prices, locations, services and team details current. Engines treat your site as the primary source for uncontested facts, so make it impossible to misquote.
- Google Business Profile and major directories next: reconcile every listing to one canonical name, address, phone and description. Each inconsistency is a fork in the record that engines have to guess their way through.
- Third-party listings you don't control: industry directories, aggregator profiles, old partner pages. Request corrections; most will update when asked, and the ones that won't can usually be outweighed by fresher, stronger sources.
- Fill the vacuum: if wrong or thin sources dominate because you've published nothing, the durable fix is publishing: service pages with explicit facts, an FAQ that answers the questions people actually ask about you by name.
+4.3 pts
lift in branded search when AI recommends a brand to unengaged users
That research finding cuts both ways, and that's the point of this playbook: AI mentions measurably move how often people go looking for a brand by name.
An engine describing you accurately and positively is compounding marketing; an engine repeating a wrong price or a dead address is compounding damage. Same mechanism, opposite sign.
Reviews: the public record AI actually reads
When an engine is asked whether you're any good, review platforms are the closest thing to evidence it can retrieve. You can't control what reviewers write, but you control the half of the record that's yours: responses.
A considered, specific reply to a critical review does double duty: it reads well to humans, and it puts your side of the story into the retrievable record right next to the complaint. A wall of ignored one-star reviews, by contrast, is exactly the kind of unambiguous signal engines find easy to summarise.
Respond to the negative ones especially, keep it factual and calm, and never pay for or fake reviews: platforms and engines are both getting better at spotting it, and the downside is a reputation problem no optimisation can fix.
A monitoring cadence a small team can keep
Reputation monitoring fails when it's designed as a daily obsession. Make it a monthly ritual instead: the same brand-question set, the same three engines, results logged in a simple sheet alongside last month's.
Escalate immediately only for genuine misinformation (wrong prices, wrong locations, confusion with another business) and treat tone drift as a quarterly conversation. Pair it with the outbound side of the coin, getting recommended in the first place, and the same monthly hour covers both.
If you'd rather see your starting position before building the habit, a free AI visibility check includes what the engines currently say about you by name.
The major providers offer feedback mechanisms and it's worth reporting clear falsehoods, but don't build your strategy on it: responses are slow and uncertain. Fixing the retrievable sources is faster and more durable, because it corrects every future answer instead of appealing one.
Differentiation is the cure: make your site and profiles unambiguous about location, ABN-registered name and services, and strengthen the distinct signals (reviews, local mentions, consistent branding). The clearer your own entity, the less the engine has to guess.
Negative ones are the priority because they're where your absence is loudest. Brief, genuine responses to positive reviews add texture to the record and cost minutes: worthwhile, but never at the expense of leaving criticism unanswered.
It varies by engine and source. Answers drawing on live retrieval can reflect a fixed page within days or weeks; anything baked into model training moves on the provider's schedule, not yours. Which is exactly why the source record, which you control, is where the effort goes.