Search behavior is changing.
For many brands, the first question is no longer only "can people find us on Google?" A growing number of users ask an AI system first:
- What is this brand?
- Is this company reliable?
- Is this product worth trying?
That makes the AI version of your brand's first impression worth checking.
Before doing more GEO work, I would not start by buying tools or publishing a large amount of new content. A better first step is a simple diagnosis: does AI recognize the brand, how does it describe the brand, which sources does it rely on, and does it carry any negative or incorrect impression?
I ran this test with our own self-media brand, Kunpeng AI Exploration Bureau, using Kimi and Qianwen on June 3, 2026. This is not a permanent ranking report. It is a repeatable diagnostic workflow.
Question 1: How does AI describe the brand?
The first prompt was direct:
Is Kunpeng AI Exploration Bureau a hands-on, practice-oriented blogger?
Please give your judgment and summarize the brand with several labels.
This question tests two things:
- Does the AI system recognize the brand at all?
- Are the labels close to the positioning the brand actually wants?
In this run, Kimi recognized the brand as a practical technical content brand. The labels it used included AI Agent practice, engineering loops, toolchain depth, verifiable retrospectives, and Windows / CLI troubleshooting.
Qianwen gave a similar answer. It described the brand as a hands-on technical blogger, with signals around real project loops, toolchain troubleshooting, open-source participation, and practical delivery.
The point is not to enjoy a flattering answer. The useful work is to split the labels into three groups:
- correct labels that you should reinforce;
- wrong labels that point in the wrong direction;
- missing labels that you want AI to recognize but it does not mention yet.
If AI cannot answer at all, your public signal may not be strong enough. If AI recognizes the brand but describes it incorrectly, your brand information may not be consistent enough.
Question 2: What sources support that answer?
The second prompt matters even more:
What is your evidence?
Which sources did you use to confirm this?
Please list the specific sources you referenced.
Brand GEO diagnosis is not really about a nice one-line answer. It is about where the AI learned the brand from.
In this run, Kimi pointed to the brand's homepage positioning, previous AI recommendation content, and the pattern of practical content. That was useful, but it also exposed a real diagnostic finding: Kimi mentioned an official homepage domain that did not match the currently maintained canonical site.
That is the lesson.
AI can recognize your brand and still rely on imperfect source attribution.
Qianwen focused more on source categories: WeChat Official Account, GitHub, technical communities, troubleshooting articles, open-source projects, benchmarks, and PR / Issue records. That direction was useful too, but the captured transcript did not expose every concrete URL. The next step is to open the source cards and verify whether those are truly the sources you want AI to trust.
A practical source audit table can look like this:
| Source type | What to check |
|---|---|
| Official site | Is it the current canonical domain? Are the title, summary, and About page clear? |
| Owned channels | Are names, bios, avatars, and links consistent across accounts? |
| Code and community | Do GitHub, PRs, Issues, and technical articles support the claimed expertise? |
| Third-party coverage | Are the references credible reports or high-quality reposts? |
| Risk sources | Old domains, scraped pages, wrong descriptions, same-name brands, low-quality aggregators |
Labels are the output. Sources are the input.
Question 3: Does AI carry a negative or incorrect impression?
Because our test brand is a content brand, the original prompt asked whether it looked like a marketing account.
For a company, product, founder, or personal brand, I would make the question broader:
Does AI have any negative or incorrect impression of this brand?
Does it see the brand as unreliable, over-marketed, unclear, or confused with another brand?
In this run, both Kimi and Qianwen said the brand was not a marketing account. Their reasoning focused on verifiable engineering detail, open-source participation, consistent topic focus, and willingness to expose failures and limitations.
That is a positive result, but the diagnosis should not stop there.
The more useful question is future risk. If the content drifts from real retrospectives into course selling, tool promotion, or an implied official position for a vendor, trust can weaken. Negative perception is not only something to diagnose after it happens. It can also be an early warning about content drift.
For most brands, this check should include:
- whether AI sees the brand as over-marketed;
- whether AI sees it as low-quality reposting or trend-chasing;
- whether AI confuses it with another brand;
- whether AI uses outdated, unofficial, or incorrect information.
What to do after the three questions
After the three prompts, you should have a simple diagnosis table:
- what AI got right;
- what AI missed;
- what AI got wrong;
- what to fix next.
Start with the places AI systems are likely to read first:
- make the homepage and About page explain clearly who the brand is, what it does, and who it serves;
- keep names, bios, avatars, and links consistent across official accounts;
- make article titles specific instead of slogan-like;
- preserve verifiable commands, logs, screenshots, PRs, Issues, and case records in technical materials;
- redirect or clean up old links, wrong domains, and outdated descriptions.
Brand GEO is not magic. It is not about manipulating AI.
It is about making public information clear enough that AI can read your brand consistently and accurately.
Full write-up:
https://kunpeng-ai.com/en/blog/geo-brand-diagnosis/
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