How we work with AI
Brand understanding
It is not enough for a brand to appear when someone types its name. We review whether ChatGPT, Gemini and Perplexity understand services, location, differentiation, reputation and use cases while the user is still comparing options.
Brand understanding:
How we work with AI / Brand understanding
17 July 2026 · 10 min read
It is not enough for a brand to appear when someone types its name. We review whether ChatGPT, Gemini and Perplexity understand services, location, differentiation, reputation and use cases while the user is still comparing options.
Key points
An assistant understands a brand reasonably well when it can describe what the company does, where it works, who it helps, what makes it different and what cautions a potential customer should keep in mind.
The goal is not for the assistant to repeat the sales pitch. The goal is to reduce confusion: no mixed services, invented locations, outdated profiles or missing trust signals.
Asking “what is Yagle” tests direct recognition. That is useful but limited. Most customers start with a need, a location, a comparison or a concrete problem.
The audit should simulate that journey: generic queries, comparisons, location questions and decision prompts.
The same questions should be tested across platforms and languages when the company serves more than one market. In Mallorca, Spanish, English and Catalan can produce different demand patterns.
Store date, platform, language, exact prompt, answer, cited sources and evaluation. Without traceability, the audit becomes an opinion.
A good audit does not only say “appears” or “does not appear”. A brand may appear with wrong positioning, without sources, with incomplete services or only in low-intent queries.
A simple matrix turns answers into work: service accuracy, location accuracy, proposition clarity, source presence, competitor comparison, perceived reputation and recommended next steps.
When an assistant misunderstands a brand, there is usually a reason: unclear pages, generic titles, outdated external profiles, missing structured data, mixed services or old mentions that outweigh the current site.
The fix is not to ask the assistant to change. The fix is to improve the sources it may use: website, company profiles, Google Business Profile, LinkedIn, service pages, relevant media, directories and useful FAQ content.
The first risk is obsessing over one screenshot. Answers change by date, language, session, location and browsing availability. Measure patterns, not one favorable or unfavorable answer.
The second risk is filling the site with text written for robots. The strongest signal is still useful content for real customers, structured clearly enough for search engines and assistants.
In one first cycle, a company can correct profiles, update business details, improve service pages, add real FAQs, review schema, check crawlability and create one or two well-evidenced content pieces.
Then the process becomes continuous: measure again, compare answers, review logs, analyze leads and check whether platforms describe the brand more accurately.