Introduction to Dageno AI Diagnostic Center (1) - Translating complex AI signals into growth

Over the past three months, we've talked to over a hundred brands expanding overseas about the same question:

"What is the biggest pain point for you when doing GEO now?"

The person in charge of going global for many brands will give the same answer: they don't know where to start.

When faced with large models such as ChatGPT, Perplexity, Gemini, without GEO data monitoring capabilities, it is difficult for brand owners to know what users are asking, why AI does not mention their own brand, and what competing products are doing right.

But often, even when data is obtained through third-party tools, we still don't know what content to optimize next, where to publish it, and how to measure the effectiveness.

In the era of traditional SEO, at least Google Search Console could tell you which keywords had rankings and which pages had traffic. But in the era of AI search, this feedback mechanism is almost broken. You can only see the final answers from AI, but not the underlying decision-making logic, the competitive landscape, or the real demand distribution.

This is the reason why Dageno AI launched the Diagnostic Center.

We will use a series of articles to comprehensively dissect the design logic, core functions, and usage methods of the diagnostic center.

1. Challenges of going global in GEO: It's not the lack of "data", but the lack of "decision-making basis".

Many people think that the difficulty of GEO optimization lies in "insufficient data".

But in fact, when you really start doing GEO, you will find that the problem is not too little data, but too many signals and unclear priorities.

Nowadays, there are various GEO tools on the market that can monitor AI responses to 100 prompts, track mentions of 20 competing products, and show performance differences across different models and regions. However, when these data are piled up, they won't automatically tell you:

• Which problem is most worth solving now?
• Why didn't AI recommend you on this issue?
• What content format should be used and which channel should it be posted on to address this issue?
• After the issue is resolved, how can we verify the effectiveness?

This is the real bottleneck. It's not that we can't see the problem, but that we don't know how to turn the problem into actionable growth initiatives.

We've seen too many teams spend a great deal of time using various GEO tools for data monitoring, only to end up deciding "let's try writing a comparison article this week" based on gut feeling. This is not a growth strategy; it's just a shot in the dark.

What the Diagnostic Center needs to do is to systematize and replicate the process of "from data to decision-making". We don't want GEO optimization to remain at the stage of "looking at data, guessing problems, and testing content", but rather hope that it can, like traditional growth, have clear diagnosis, well-defined priorities, and verifiable execution paths.

2. Diagnostic Center Module 1: AI Mentions & Avg Position

The Diagnostic Center is a multi-dimensional growth diagnostic system. In this article, we will introduce the first module, which is also the most core capability of the system:

Content production suggestions based on AI Mention (AI Mention) and Average Position (Avg Position).

AI Mention

The number of times the brand is explicitly mentioned in the AI answer body under a set of monitoring prompt words, as a proportion of the total number of answers (citations are not counted).

Average Position

In the responses where the brand is mentioned by AI, the average position of the brand in the recommendation list, with a smaller number indicating a more prominent position.

Then, the system automatically determines that it belongs to the following AI mention scenarios:

• was not mentioned at all
• mentions instability
• is mentioned, but ranked lower

Over the next few weeks, we will gradually add more dimensions such as AI citation analysis, public opinion monitoring, and social media signals at the Diagnostic Center. However, we chose to launch this module first because it directly answers the most pressing questions of most teams:

"What should I do now to improve the brand's visibility and recommendation priority in AI search?"

Detailed Explanation of Specific Functions

Simply put, the Diagnostic Center will help you with three things:

1. Identify real user issues

We will identify the questions that users are actually asking in large models based on your industry, brand positioning, and competitive environment. These are not the questions you guessed, but those with real search volume and real demand.

Each row represents a real prompt. You will see how many searches (Volume) this question has in the current month, the brand's mention rate (AI Mention), the current ranking status & question type (e.g., mentioned but ranked low), and what the direct competing products are.

2. Determine which issues are worth prioritizing for resolution

Not all issues are worth addressing now. Some issues have a high search volume, but your brand awareness foundation is too weak to achieve a breakthrough in the short term; some issues you already rank second in, and only need a piece of comparative content to surpass competing products; some issues seem relevant, but actually have a very small demand, making the input-output ratio not cost-effective.

We will assign a Priority to each issue based on multiple dimensions. This priority is not a simple weighted average, but a comprehensive judgment that combines demand popularity, competition difficulty, brand status, and growth potential.

3. Directly provide an actionable content strategy

When you click into a high-priority issue, the system won't just tell you "There's an opportunity here," but will continue to proceed as follows:

• Why does this opportunity exist? Why isn't AI currently prioritizing recommending you?
• What is the most worthwhile content type to produce for this opportunity?
• If you want to write a piece of content, what structure should you use, what key points should you cover, and from what perspective should you approach it?
• Is this issue better addressed through official website content, third-party reviews, or community discussions?
• How long will it take to see the results?

The time of results appearance and the expected cycle are determined based on historical data and empirical models accumulated during our customer service practice and overseas GEO data support process.

This is not a simple analysis report, but an implementation plan that can be directly submitted to the growth team or external service providers.

3. A Seriously Underestimated Indicator: Volume

Among all the metrics at the Diagnostic Center, we would like to specifically talk about Volume (actual search volume).

This indicator may seem very basic, but it is actually the cornerstone of the entire GEO optimization.

Currently, in the global GEO tools market, only Dageno and Profound can provide real AI search volume data.

Most tools either do not have this metric at all, or use the keyword search volume of traditional search engines, or rely on model-based estimates.

But these three are completely different things, traditional search volume ≠ AI search volume.

When a user searches for "CRM software" on Google and asks "Which CRM is more suitable for a 50-person SaaS team" on ChatGPT, the underlying demand intensity, decision-making stage, and information expectations are completely different. The former may just be a general inquiry, while the latter has already entered the specific selection stage.

More importantly, without real search volume, you simply cannot determine whether a problem is worth tackling.

Suppose there are two questions:

• Problem A: Your brand is not mentioned at all, but this problem only has 50 searches per month
• Problem B: Your brand ranks third, but this problem has 5,000 searches per month

If you only look at "whether it has been mentioned", you will prioritize Problem A. But if you look at the search volume, the optimization value of Problem B may be 100 times that of Problem A.

That's why we consider Volume as one of the core dimensions for priority calculation. We don't want teams to spend time on optimizations that "seem problematic but actually have little demand". The essence of growth is to amplify leverage, not to fill all gaps.

Our Volume data comes from real AI search behavior tracking (purchased from a third-party compliant plugin), covering 7 major mainstream large models (ChatGPT, Gemini, Perplexity, etc.), updated monthly by region. This enables you to clearly see which questions have real, large-scale demand behind them.

4. From Diagnosis to Execution: A Complete Growth Closed Loop

We believe that a truly useful growth tool should help you complete the entire process from diagnosis to execution.

So in the Diagnostic Center, once you confirm that you want to solve a certain problem, you can directly enter the content generation process.

Our writing Agent does not simply "generate an article", but first performs a layer of strategic translation:

• Who is this article written for? (Target audience)
• What question needs to be answered? (Core requirement)
• What goals need to be achieved? (Brand goals)
• From what perspective is it written? (Brand perspective)
• What boundaries cannot be crossed? (Compliance and brand tone)

Then, the Agent will pull brand context (positioning, features, competing product information), supplement external facts (policies, data, public information), and finally enter the formal writing and quality check process.

The content generated in this way is not written for the sake of writing, but is written around brand objectives, competitive scenarios, and real needs.

More importantly, the entire process is traceable. You know why this content is written, who it is written for, what problems it aims to solve, and where it should be published. This way, even external service providers can quickly understand the background and start execution.

In the future, we will also implement automated distribution, which can adapt to content formats of different platforms with a single click and publish to We Media or internal blog systems.

From problem discovery → priority determination → content generation → publication and distribution → effect tracking, the entire process is complete.

Conclusion

Some may ask: Why launch the Diagnostic Center now?

Because we have observed that the GEO market is experiencing a critical turning point.

Early GEO optimization was more like an "experiment". Everyone was trying various methods to see what worked and what didn't. During this stage, what you needed was flexibility and the ability to iterate quickly.

But now, more and more brands are starting to treat GEO as a long-term, systematic growth channel. This means that you can no longer make decisions based on "trial and error", but instead need a reliable and replicable optimization process.

This is the problem that the diagnostic center aims to solve ⬇️

We hope to help the brand growth team advance GEO from the "experimental phase" to the "scaled growth phase".

Meanwhile, we also want to share with you that since the product was launched, we have been using Dageno AI to optimize our own official website.

For example, throughout April, we implemented the optimization suggestions provided by the Diagnostic Center. The final results were not only reflected in the GEO data.

Natural search traffic has also significantly increased (the following screenshots are from Semrush, and you can check them out on your own).

Welcome everyone to join us in monitoring the data changes of Dageno AI, which is also part of our build in public.