A partner at a specialized law firm told me something recently that stayed with me.
He had just come back from a conference where one of the firm’s best prospects, the general counsel of a large private lender, said:
“We looked for you on AI and could not find you.”
This is not a general law firm trying to sound specialized. They do loan closings for private lenders. All day. Their clients are private lenders. Their practice is built around this narrow, specific work.
But when someone searches for something like “fix and flip loan law firm,” they do not show up where it should.
The answer was full of related-but-wrong options: general real estate attorneys, firms outside the markets the buyer cared about, and companies connected to renovation disputes or title checks.
The firm that actually does this work just wasn’t there.
Their clients know them for handling private lending loan closings. But a new buyer asking AI about that need may not find them at all.
Your sales team may know exactly who you compete with. But AI may be giving buyers a different list.
We saw a similar pattern in a GEO audit for a specialized Kubernetes infrastructure company.
This was not a company with a small content footprint. They had 500+ pages, 400+ blog posts, case studies, ebooks, and 300+ YouTube videos.
Their sales team knows who they compete with in real deals: other Kubernetes management and multi-tenancy tools, usually companies of roughly similar scale.
But when we tested questions a buyer might ask early in the search, AI kept pulling in a different set of names: AWS, Azure, and NVIDIA.
Those were not random names. AWS, Azure, and NVIDIA do offer parts of what buyers care about here, including GPU efficiency, infrastructure management, and support for complex cluster environments.
But those are capabilities inside much larger platforms. The company we were auditing was a focused Kubernetes infrastructure tool.
So the buyer is not just seeing different company names. AI is making the category look different from how the company would explain it.
The answer makes the big platforms seem like the place to start. The specialist may be a better fit for the actual problem, but AI has not made that clear yet.
The company may be strong in the actual sales cycle, but missing when the buyer is still figuring out what kind of solution they need.
Being mentioned is not enough if AI has already explained the market without you
In another audit, we saw a different version of the same issue.
This was a cloud cost management company with a strong product, real customers, and a clear buyer.
When we tested early research prompts, AI often started with the native tools: AWS Cost Explorer, Azure Cost Management, and Google Cloud’s cost tools.
That was not wrong. Those tools are part of how many teams begin managing cloud costs.
But they are also built into much larger cloud platforms. The company we were auditing was a specialist tool built for teams that need more flexibility, better governance, cross cloud visibility, or easier use across teams.
The company did show up in the answers, but usually after the native tools had already been explained as the natural starting point.
The buyer saw the company, but only after AI had made the native tools feel like the obvious starting point.
The company was there. But by then, AI had already made the native tools seem like the obvious place to start.
More content is not always the fix
After seeing this, the easy conclusion is: we need more content.
Sometimes that is true. But the Kubernetes example is a useful reminder that volume alone does not solve this.
That company was already publishing a lot: hundreds of pages and blog posts, case studies, ebooks, and a large YouTube library.
A firm can publish a lot and still have AI describe it too broadly.
A product can be mentioned and still show up too late, or next to the wrong alternatives.
That is what these audits keep showing us. The issue is often not whether the company has published enough. It is whether the published material makes a few key things unmistakable: who the buyer is, what problem they are solving, when the company is a good fit, and which alternatives it should be compared against.
The competitors you track may not be the ones AI shows your buyers
Most companies know the competitive landscape they deal with in real sales conversations.
Sales knows the names that come up in deals. Marketing knows the search terms. Product knows the alternatives buyers ask about. After enough sales calls, everyone inside the company carries a pretty clear map of the market.
AI works from whatever is visible outside: your website, third-party pages, reviews, comparison articles, documentation, videos, and whatever else is visible.
Sometimes those two pictures match.
Often, they do not.
For a specialist law firm, AI may answer with broad real estate law when the buyer needs private lending loan closings.
For a Kubernetes company, AI may turn a focused infrastructure question into a hyperscaler conversation.
For a cloud cost company, AI may make native platform tools feel like the default before the specialist appears.
This does not mean AI is making things up.
It means AI is assembling the answer from the signals it can see. If your public material talks broadly, AI will usually answer broadly. If your niche is not made explicit, AI may not infer it.
‘Do we show up?’ is only the first question.
Most teams start with the simplest question: do we show up when a buyer asks about our category?
That matters. But it is only the first question.
You also need to know who appears before you, which companies are placed next to you, what category AI puts you in, and what reason it gives the buyer to consider you.
A buyer is not only asking AI for names. They are also asking, ‘How should I think about this problem?”
If AI shows buyers the wrong set of alternatives, the buyer may never get to the reasons you are actually a better fit.
You may be planning around the competitors you see in sales calls, while AI is sending the buyer down a different path before you ever get a chance.
Value AI Labs runs GEO audits for B2B companies.
If you want to see what AI is telling your buyers about you, the audit starts at $1,000.
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