AI named the brand. What picture did it create?

A brand portrait here does not mean a logo, a writing tone, or an editor's emotional reaction. It is the compact set of meanings a reader can take away from an answer: what the product is, who it is for, why someone might choose it, and what trade-off comes with that choice. The method works with visible answer text and does not try to guess the model's internal reasoning.

The audit is not a hunt for one approved phrase. Its simpler job is to notice when an answer preserves the intended positioning, when it simplifies it without material harm, and when it moves the reader's expectation in another direction.

The metric 'brand named' answers only one narrow question. Two answers can both contain the name while creating different expectations. For example, 'a flexible tool' may sound like a benefit, while adding 'difficult for beginners' can change the reader's decision.

This does not make a mention useless. A mention remains a separate observable event. It simply needs a second layer beside it: what picture the reader received and which exact answer fragment created it.

Do not give the answer one overall label such as 'positive' or 'negative'. It hides the reason and is hard for two editors to compare. Instead, copy four concrete elements from the answer. If an element is absent, record 'not stated' rather than completing it from memory.

Start with one choice scenario rather than the brand's entire positioning map. Prepare three prompt types for that scenario: who the brand suits; how it differs from named alternatives; and what experience and limits to expect. Keep the wording fixed within one measurement series.

For every run, save the service, exact mode, model version when displayed, language, region, date, account state, and whether web search was enabled. Do not merge ChatGPT, Perplexity, Gemini, Claude, Alice, GigaChat, Grok, and DeepSeek into one row. Their answers belong to different observation environments and become comparable only under a stated rule.

1. Save the exact prompt before the first run.
2. Save the full answer, not only the line containing the brand.
3. Record displayed links and page addresses separately.
4. Repeat each run a number of times chosen in advance.

For each of the four fields, use one of four labels: aligned with the public positioning; acceptable simplification; distortion; or unsupported by a visible basis. The first three compare the answer with a reference written in advance. The last describes the boundary of the audit, not necessarily a model error.

The reference should not be a private brand book that nobody outside the company can see. Before measuring, create a short public reference: one category, one core audience, one differentiator, key limits, and links to pages where each point is stated in ordinary language.

Preserve three layers separately: the sentence about the brand in the answer; the displayed link, if one exists; and the passage on the opened page that actually supports that sentence. A domain in a source list does not yet show which claim it supports.

If no link is displayed, do not turn that absence into proof of an error or lack of knowledge. The accurate record is: the saved answer did not show its support, so the reviewer cannot establish it from the interface. That is an evidence boundary.

Imagine a fictional service whose public promise is an easy start for small businesses. An answer calls it 'a flexible platform for large teams that requires configuration'. The mention counts, but the matrix reveals three shifts: a different audience, the missing easy-start differentiator, and the label 'requires configuration'.

This does not immediately justify saying that the AI system 'does not understand the brand'. First check whether the website, third-party reviews, and metadata in Google Play, App Store, and RuStore contradict one another. Then repeat the measurement to see whether the portrait is stable or appeared once.

A team cannot directly edit another system's answer. It can inspect the public context available to people and search systems. On the website, connect category, audience, differentiator, and limits in one clear explanation. In external material, demonstrate the differentiator instead of repeating a slogan.

If the brand has an app, align the same meaning on the brand's pages in Google Play, App Store, and RuStore within each platform's rules. This does not promise that a later answer will change. It only removes contradictions from material that can be found and read.

• Website: one clear page covering audience, differentiator, example, and product boundaries.
• External coverage: examples and comparisons that make the reason to choose the brand concrete.
• Google Play, App Store, and RuStore: aligned audience and real differentiator in the available metadata.

After revising public pages, run the same prompts under the same conditions. Compare not the beauty of the prose, but the share of answers in which each field preserves the meaning of the public reference. Decide in advance how many repeats are needed and what change will trigger further investigation.

Even a stable shift does not prove that one specific edit caused it. The index, model, search mode, or available pages may have changed at the same time. Record the result as an observed change. Measure rank, displayed links, referrals, and human actions separately.

The target is not a word-for-word copy of the brand book. A useful answer preserves meaning: the reader understands the correct category, audience, real differentiator, and product boundaries without inheriting a persistent foreign label.