AI SEO Is the New Visibility Game. Here's How I Picked a Tool For It.

A few months ago I caught myself doing something I do constantly now and had never really clocked: I needed to vet a vendor, and instead of opening Google, I opened ChatGPT and asked it straight out. No ten blue links. Just an answer that named three companies and told me why.

That small habit, multiplied across millions of people, is quietly rewriting one of the oldest games in business — how you get found.

For fifteen years the answer to "how do we show up when someone's looking for us" was SEO: rank on Google, win the click. That game still exists. But a new one is forming on top of it, and as an operator I'd rather be early and a little wrong than late and certain. So I did the homework. This is what I learned — and how I chose a tool to do something about it.

What "AI SEO" actually means

People call this answer-engine optimization, generative-engine optimization — AEO, GEO, "AI SEO." Strip the jargon and it's simple. Classic SEO is about ranking in a list of links. AI SEO is about whether your brand gets named, cited, and recommended inside the AI's answer itself — in ChatGPT, Claude, Perplexity, and Google's AI Overviews.

The difference matters more than it sounds. A results page gives you ten options. An AI answer gives you one or two, framed with reasons, delivered like advice from a trusted source. If your competitor is named and you aren't, you don't lose a ranking position — you lose the consideration entirely. The user never sees you were an option.

In classic search you compete for a click. In AI search you compete to be the recommendation — and the recommendation is a list of one or two, not ten. Invisibility there is far more expensive than a low ranking ever was.

The tricky part: this surface is dark to you. The answer changes by prompt, by phrasing, by user, by day. You genuinely don't know whether you show up for "best [your category] platform" unless you go measure it. That measurement problem is why this category of tools exists.

What these tools do

The two I evaluated seriously, for work, were Profound and Athena. At the category level they do the same jobs: run large batches of realistic prompts against the major models and log where your brand shows up; surface which questions you're strong on and which you're invisible for; show who's getting named instead of you; and point toward the content and positioning moves that tend to nudge the answers your way.

If classic SEO gave you Search Console, this is the equivalent dashboard for the AI layer. You stop guessing whether the models know who you are and start seeing it.

One honest caveat: both tools are good at telling you where you stand. The science of moving an AI answer is younger and messier than the science of moving a Google ranking — that playbook is still being written, by everyone in the space at once.

Profound vs Athena

I'm deliberately not quoting pricing — those numbers move, they're negotiated, and ours are ours. But here's the texture of the decision.

On raw features, this was closer than the marketing on either side would have you believe. Anyone who tells you one is dramatically ahead on capability is probably selling something. So the decision came down to commercial fit and speed-to-value — which is exactly the right way to decide in an emerging category.

Why we chose Athena

We went with Athena, mostly on the commercial terms. That can sound unserious if you evaluate on features alone. It isn't. When you're buying a mature tool you have to have, you pay what it costs and optimize for capability. When you're placing an early bet on unproven upside, the smartest move is to lower the cost of being wrong and shorten the time to learning something.

Friendlier pricing and flexible terms do exactly that. If AI SEO matters as much as I suspect, we're already in motion. If it plateaus, we didn't overcommit to find that out.

In an emerging category, the right pick is as much about commercial fit and speed-to-value as raw features. Lower the cost of being wrong, shorten the time to learning, and you can place a real bet without betting the farm.

The early signal has been encouraging. I won't throw invented percentages at you — it's too soon for the clean before-and-after chart, and I'd rather be trusted than impressive — but the results have come faster than I expected and they point the right direction.

The operator takeaway

Step back from the two tools, because they'll both evolve and there will be more of them by the time you read this.

The shift underneath is the thing to internalize. "How do customers find us" now has a second half: what does the AI say when someone asks about us — or our category — and is that answer in our favor? Most companies have no idea. They've never looked. That's an opening.

You don't need to bet big to start. Get honest visibility into where you stand, pick a tool whose terms let you experiment without overcommitting, and start learning before your competitors do.

If you're wrestling with this for your own company — or you've found a tactic that's working — drop it in the comments, I read all of them. And if you want to talk through AI visibility for your specific situation, reach out through the contact page.