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. No scrolling. 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 post is what I learned — and how I ended up choosing a tool to actually do something about it.

What "AI SEO" Actually Means

Let me define the thing before I sell you on caring about it, because the acronyms multiply fast.

People are calling this answer-engine optimization or 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 and lets you choose. An AI answer often gives you one or two, framed with reasons, delivered like advice from a trusted source. If your competitor is named in that answer and you aren't, you don't lose a ranking position — you lose the consideration entirely. The user never even 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 usually a list of one or two, not ten. Invisibility there is far more expensive than a low ranking ever was.

The tricky part is that this surface is mostly dark to you. You can't open a dashboard and watch where you stand the way you can with Google rankings. The answer changes by prompt, by phrasing, by user, by day. You genuinely don't know whether you're showing up for "best [your category] platform" unless you go measure it. That measurement problem is the whole reason this category of tools exists.

What These Tools Do

A handful of companies have sprung up to make that dark surface visible. The two leading ones I looked at hard, for work, were Profound and Athena. At the category level they do a similar set of jobs:

• Track your presence across AI answers.: They run large batches of realistic prompts against the major models and log where your brand shows up, where it gets cited, and where it's missing entirely.
• Surface the prompts that matter.: Not just "do we appear," but for which questions — so you can see you're strong on one type of query and invisible on another.
• Watch the competition.: They show who's getting named instead of you, and how often, so you have a real share-of-voice picture instead of a vibe.
• Guide what to do about it.: They point toward the content, sourcing, and positioning moves that tend to nudge those answers in your favor over time.

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

I want to be honest about the maturity here. This is an emerging category. The science of *moving* an AI answer is younger and messier than the science of moving a Google ranking. Both tools are good at telling you where you stand. The playbook for changing it is still being written — by them, by us, by everyone in this space at once.

Profound vs Athena

Here's the honest side-by-side from actually evaluating both. I'm deliberately not going to quote you pricing or contract figures — those move, they're negotiated, and ours are ours. What I can tell you is the texture of the decision.

I'll say the quiet part out loud: on raw features, this was closer than the marketing on either side would have you believe. Both cover the fundamentals well. Anyone who tells you one is dramatically ahead of the other on capability is probably selling something.

So the decision didn't come down to a feature checklist. It came down to commercial fit and speed-to-value — and that's exactly the right way to decide in an emerging category.

Why We Chose Athena

We went with Athena, and the reason was mostly the commercial terms.

That can sound unserious if you evaluate on features alone. It isn't. Here's the operator logic. When you're buying a mature, proven tool you *have* to have, you pay what it costs and optimize for capability. When you're placing an early bet in a category that's still forming — where the upside is real but unproven — the smartest move is to lower the cost of being wrong and shorten the time to learning something.

Friendlier pricing and flexible contract terms do exactly that. They let me run a genuine experiment instead of signing up for a conviction I haven't earned yet. If AI SEO turns out to matter as much as I suspect it will, we're already in motion and ready to lean in. 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.

And the early signal has been encouraging. I'm not going to throw invented percentages at you — it's genuinely 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 for something this new, and they point the right direction. Enough to make me confident this was a smart place to spend attention, not just budget.

The Operator Takeaway

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

The thing I'd actually push you to internalize is the shift underneath. The question "how do customers find us" now has a second half that didn't exist a couple of years ago: what does the AI say when someone asks it about us — or about our category — and is that answer in our favor? Right now most companies have no idea. They've never looked. That's an opening.

You don't need to bet big to start. You need to get honest visibility into where you stand, pick a tool whose commercial terms let you experiment without overcommitting, and start learning before your competitors do. That's the same operator instinct that shows up in everything I write here — do the homework, show up better prepared than the room expects, and treat being early as an advantage rather than a risk.

If you're wrestling with this for your own company — or you've found a tool or tactic that's working — I genuinely want to hear it. Drop it in the comments, I read all of them. And if you want to talk through how to think about AI visibility for your specific situation, reach out through the contact page. It's exactly the kind of conversation I enjoy most right now.