AI visibility audit
I run a baseline audit of what AI assistants tell your buyers about your company — a fixed prompt set across platforms and languages, source tracing for every error found, and a written report separating what can be fixed from what can't.
100
+Completed projects
Websites, landing pages and SEO projects for businesses in different niches and markets.
15
Years of experience
Hands-on experience in SEO, website development and digital strategy.
17
+Industries covered
From local services and startups to real estate, e-commerce and B2B projects.
3
Working languages
English, Polish and Russian — with content and structure adapted to each market.
What the AI visibility audit includes
A prompt set built for your market
Ten to twenty-five prompts phrased the way your buyers actually phrase things — branded, category, comparison and problem-based. Written for your business, then frozen, because changing the set between runs destroys comparability.
A run across platforms and languages
ChatGPT, Perplexity and Google's AI answers, in a clean session, separately for each language your market uses. Presence in one implies nothing about the others, and a company is often described correctly in one language and wrongly in another.
Source tracing for every error found
Errors come from somewhere: an outdated directory listing, an old press item, a review profile filled in at registration, a competitor's comparison page. I find which one rather than guessing.
Separation of what's fixable from what isn't
A claim retrieved from a live page can be corrected. A claim coming from the model's own memory can't be, until that model is retrained. Knowing which is which is what stops you spending months on something that was never going to respond.
A written report with priorities
What's said, where it comes from, what to fix first, what to leave, and what can't be touched. It's a complete deliverable — several clients have taken the report and implemented it themselves.
Measurement set up so you can repeat it
The frozen prompt set, the logging template, the citation report in Bing Webmaster Tools and a referral segment for assistant traffic. You can re-run the audit yourself next month, with or without me.
Why an AI visibility audit is a measurement, not a screenshot
Almost everyone's first encounter with this is the same: someone types the company name into ChatGPT, reads the answer, and either relaxes or panics. Both reactions are premature, because a single answer isn't evidence of anything.
Assistant answers vary between runs. Ask the same question twice in one afternoon and the companies named may differ, the description may shift, the sources may change. That variation is a property of the systems, not a fault. It also means one check produces an impression, and impressions are a poor basis for spending money.
An audit is the same question asked in a controlled way — a fixed set, a clean session, every platform separately, every language separately — and recorded so that next month's run can be compared against it. That comparison is the whole value. Without it you have anecdotes.
What an AI visibility audit usually finds
Three patterns recur.
You're not named at all. In which case the problem is visibility, not accuracy, and the work is different: stating your facts unambiguously, structured data, confirmation from independent sources.
You're named and described incorrectly. Outdated prices, services you dropped, the wrong geography. This is the correctable kind, provided the source is retrievable.
You're described correctly, in the language of your category. The most common and the least noticed: nothing is wrong, but nothing distinguishes you either, because everything distinguishing was written as imagery rather than as fact, and imagery doesn't survive summarisation.
What an AI visibility audit can't do
It can't guarantee that any answer will change. Where a claim comes from a live page, correcting that page usually works, on a timeline measured in weeks. Where a claim comes from the model's training data, source corrections don't reach it at all until that model is retrained — on the platform's schedule, not yours.
The audit's job is to tell you which of those you're dealing with, before you spend anything on fixing it. That distinction is the single most useful thing in the report, and it's the reason the audit comes before the work rather than instead of it.
The method itself is written up in detail in my article on checking what AI assistants say about your company. You're welcome to run it yourself — the article holds nothing back. This service is for the case where you'd rather not spend the day on it, or where you want the source tracing done by someone who does it regularly.
This is one of four services under AI-Ready SEO & GEO, the broader work of making a business legible and accurate to AI search.
How the AI visibility audit runs
We define your buyers and markets
Who your clients are, how they'd describe their situation to an assistant, which languages and countries matter. The prompt set comes out of this conversation, not out of a template.
I run the baseline
Every prompt, every platform, every language, in clean sessions, logged with the sources cited and the date. Screenshots kept, because answers change.
I trace the errors
Each incorrect claim followed back to the page producing it, and each one classified as correctable or not.
You get the report
What's said, where it comes from, what to fix in what order, and the frozen prompt set with the logging template so the measurement can be repeated.
