Direct answer
What should you actually check before choosing an AI visibility or AEO platform?
Look past the feature list and check the evidence discipline underneath it: does it separate real observations from simulation, does it distinguish a failed check from a genuine miss, does it reason at the page level instead of one domain-wide score, does it verify fixes instead of just recommending them, and does it interpret findings against your actual business rather than a generic template. Most tools in this now-crowded category skip several of these.
01
A long feature list doesn't tell you whether a platform's underlying evidence is trustworthy.
02
The most important checks are about how a tool handles failure and ambiguity, not how it presents success.
03
Growthract was built around these six criteria directly, rather than having them added after the fact.
The AI visibility category went from a handful of early entrants to a crowded market in the space of a year. Almost every one of them will show you a dashboard with a score on it.
We've already written about the shape of that market of tools — enterprise citation platforms, mid-market prompt monitors, SEO-suite add-ons. What that comparison couldn't tell you is which one to trust once you're actually looking at its output. That comes down to a smaller, less flashy set of questions than most buying checklists ask.
1. Does it separate real observations from simulation?
A lot of AI-visibility numbers you'll see were never actually checked against a real AI system. They were estimated — modeled behavior, generated to look like an observed result. That's not automatically dishonest, but it needs to be labeled, and most dashboards don't bother. We wrote a full explanation of why this distinction matters. Ask any vendor directly: is this number from a live request, a captured real session, or a simulation? If they can't answer cleanly, treat everything else in the report with more suspicion.
2. Does it tell you when a check failed, instead of quietly recording a miss?
A request to an AI provider can fail for reasons that have nothing to do with your brand — a timeout, a rate limit, a grounding layer that didn't activate. A tool that can't tell the difference between that and a real, completed check that found nothing will eventually convince you of things that aren't true. This is the same principle whether the failure happens testing an AI assistant or crawling a page that won't load.
3. Does it reason page by page, or just hand you one domain-wide score?
A single number for your whole site averages away exactly the information you need — which specific pages are actually broken. We've covered why page-level evidence beats a single score, and why a tool should also be looking at evidence about your brand beyond just your own website. A tool that only ever talks about "your site" in the aggregate is skipping most of the useful detail.
4. Does it verify fixes, or just hand you a checklist and disappear?
Plenty of tools are good at generating recommendations. Very few go back and check whether the recommendation actually got implemented correctly. That gap is where months of unverified "fixed" tickets quietly accumulate. Ask whether a platform can re-check the same evidence after you've made a change, not just recalculate a new score from scratch.
5. Does it interpret findings against your actual business?
The same missing schema type matters differently for a self-serve product than for an enterprise seller — we've written about why identical evidence deserves different priority depending on who the business actually sells to. A tool that treats every B2B SaaS company identically is giving you generic advice wearing a personalized-looking dashboard.
6. Can you actually interrogate the report, in plain language?
A 60-page PDF is only useful if someone reads all of it. Being able to ask a direct question and get an answer grounded in your own evidence — not a generic AI chat guessing at an answer — changes whether a report actually gets used or just gets filed away.
Why we're bringing this up
This isn't a neutral list — we built Growthract around these six things specifically, because the market data backs up why they matter. Our own 2026 benchmark of 100 B2B SaaS websites found real, checkable gaps — inconsistent schema adoption, uneven llms.txt uptake, one company blocking every major AI crawler outright — that a domain-wide score would have smoothed over entirely. Every one of the six criteria above exists because we ran into a way that skipping it produces a misleading report.
None of this means Growthract is the only reasonable option, or that every alternative fails every check. It means these are the right questions to ask, of us or anyone else, before trusting a platform's output enough to act on it.
Run the checklist against us
See where Growthract's evidence actually comes from.
Run a free diagnostic and judge the report against these six criteria yourself — labeled evidence states, page-level findings, and a verification step built in.
A couple of follow-up questions
Should I ask every vendor these six questions directly?
Yes. A vendor confident in their evidence discipline should be able to answer all six clearly, without needing to hedge or change the subject.
Does passing all six guarantee a platform is right for me?
No — it's a trust filter, not a fit filter. You'll still want to weigh price, coverage and workflow fit separately, but these six rule out tools whose output you shouldn't trust in the first place.
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