SPRKL Team
Ask three AI assistants what your company does and you may get three different answers. A brand scan shows you which one is right, which one is out of date and which one describes someone else.
Your buyers have stopped starting with a page of search results. More of them open ChatGPT, Gemini or Perplexity and ask a direct question. Who does this well in Dubai? What does this company actually offer? Is it a fit for us? The answer they get is a summary of your brand that you did not write.
We covered why that matters in The Invisible Shortlist. This piece is about the practical step that comes before any fix: reading what the assistants say about you today. That is what an AI brand scan does.
What a brand scan actually is
A brand scan is a structured set of questions put to several AI assistants, with the answers compared side by side. It is not a ranking report and it does not need tracking software. It asks the questions a real buyer would ask, in the way they would ask them, and records what comes back.
A useful scan covers three kinds of prompt:
Identity prompts. “What is [brand]?” and “What does [brand] do?” These show whether the assistant knows you exist and how it frames you.
Category prompts. “Best [service] firms in [city]” or “Who should I hire for [problem]?” These show whether you appear when the buyer does not know your name yet.
Comparison prompts. “[Brand] vs [competitor]” and “Is [brand] right for [use case]?” These show how you are positioned against the alternatives.
Running the same set across ChatGPT, Gemini and Perplexity matters because each one builds its answer differently. Perplexity shows its sources openly. ChatGPT and Gemini can blend what the model learned in training with live search results. The differences between them often tell you more than any single answer.
Five things a scan usually reveals
Every brand is different, but the same patterns come up again and again.
Absence. The assistant has little to say, or mixes you up with a company that shares your name. This is common for younger firms and for brands whose site says little in plain text.
An old version of you. The description matches what you offered two years ago: the old service line, the previous office, the partner who left. Models repeat what the web said most often, not what is current.
The wrong category. You run a strategy consultancy and the assistant calls you a digital agency. Category errors are costly because they decide which shortlists you are placed on.
Someone else’s proof. In category and comparison answers, competitors are named with specifics while you get a generic line. Usually they have published clearer evidence: case studies, named sectors, structured service pages.
Borrowed sources. The assistant leans on a directory listing, an old press article or a review site instead of your own pages. Your site is not yet the source of truth for your own brand.
Why the answers drift
AI assistants do not read your brand guidelines. They assemble an answer from whatever is easiest to find and easiest to parse: your website copy, your structured data, third-party listings, reviews, articles and social profiles. When those sources disagree, the model picks a version, and it is not always yours.
This is the same gap-filling behaviour we described in Hallucination Insurance. A scan simply makes the gaps visible, one prompt at a time.
What to do with the results
A scan is only useful if it turns into a short list of fixes. We sort findings into three groups:
Fix the source. Rewrite your homepage, About and service pages so they state plainly who you are, what you do, for whom and where. Add structured data so machines read the same facts people do.
Fix the echoes. Update directory listings, social bios and partner pages that still carry the old description. Consistency across sources is what lets a model commit to one answer.
Build the missing proof. Where competitors win on evidence, publish your own: case studies with clear outcomes, named sectors, real methods.
Then scan again. Assistants refresh what they know on their own schedules, so treat the scan as a recurring check, not a one-off audit. Our 90-day AEO roadmap shows how this fits into a wider plan, and making the check a routine rather than a task someone has to remember is what our Flow stack is built for.
Where SPRKL fits
Scanning and correcting how machines read a brand is the core of our Signal stack. When the problem runs deeper, for example a positioning that cannot be explained in one sentence, the work starts in Shift.
If you want to see your own starting point, the free AI Brand Scan at signal.sprkl.co runs identity, category and comparison prompts for your brand and shows the answers side by side.
The SPRKL Take: Your brand now has a second description, written by machines and read by your buyers before they ever reach you. You cannot edit it directly, but you can read it, correct the sources it learns from and check again. Start by reading it.
