Running SEO and GEO/AEO audits is an excellent use case for AI, especially with recent models that have agentic capabilities. These models have extensive knowledge bases and can perform multistep processes, such as extracting webpages, reviewing data, and formulating recommendations.
But before running your SEO or GEO/AEO audit in Claude or ChatGPT, have you considered whether the model has everything it needs to provide a good response?
You might be shocked to discover that state-of-the-art models provide detailed recommendations without the most basic information, including Google SERPs, keyword volumes, or even the ability to fetch URL content. For example:
As CEO of an SEO/GEO agency in the B2B tech space, I receive AI-generated audits from clients and prospects daily. I call them “naive audits.” They appear detailed and impressive, but they quickly fall apart when you ask basic questions:
- What was this based on?
- Where did the AI get this data?
- What is the methodology?
I’m writing this article to help explain the gap between what you expect from an AI-based audit and what you get in practice. My goal isn’t to discourage you from using AI for these purposes — you definitely should. Rather, I propose a framework with three elements to help you get AI audits right, ensuring they’re rooted in reality and provide real value.
‘Naive’ SEO audits: What can go wrong?
Let’s take a simple example: generating SEO recommendations for an existing blog. This should be an easy task for an advanced language model.
I took a blog written by one of our clients about shortages in the flash storage industry. It’s a timely topic, and the article could probably attract traffic if optimized properly.
Here is the prompt I gave Claude Opus 4.7 (with Adaptive Reasoning).
Claude thinks for a bit and provides a detailed 1,600-word report with everything we should fix in the article. See the full response here. I’m showing the highlights below. Looks promising!
But here’s the first warning signal: Claude hints that it had to infer the current structure of the article. Let’s check that out. I asked whether it had actually read the article:
Surprise 1: Claude couldn’t actually read the article. It didn’t admit that until I asked. Instead, it relied on search snippets. The entire analysis was based on a few snippets rather than the full content, which means many of the findings were probably irrelevant.
Now let’s take it one step further. I asked Claude to come up with a main keyword, and it suggested [intelligent data tiering]. Does that keyword actually have search volume?
Surprise 2: Claude doesn’t have access to search volumes, and when thinking about it, it admits the keyword is unlikely to have volume. Here is what Semrush had to say.
So the results were based on “inferred” content of the page and a keyword that isn’t really searched by…
Source link
Disclaimer
We strive to uphold the highest ethical standards in all of our reporting and coverage. We blogs.grocliq.com want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.
Website Upgradation is going on for any glitch kindly connect at [email protected]
