AnswerPresence by So What Media
AEO evidence is easy to collect. The hard part is knowing whether it matters.
AnswerPresence exists for the bit after the screenshot, the tool report, and the internal debate.
We benchmark what AI tools say about your category, compare your brand against the competitors your buyers are likely to see, and turn the pattern into one commercial recommendation: act internally, scope a pilot, run a diagnostic, or leave it alone for now.
The value isn't in the readings.
It's in the judgement.
Why AnswerPresence exists
AI tools rarely hand a company one neat visibility problem.
They hand it a pile of signals.
A competitor appears in ChatGPT. A third-party list gets cited by Perplexity. Gemini understands the category but leaves your brand out. Claude describes the problem well, but learns the answer from a source you don't control.
Useful signals.
Not a decision.
Enough to brief a team, buy a tracking tool, or fund a 90-day pilot?
Not on its own.
That's the gap AnswerPresence is built around. The audit isn't trying to collect every possible AI answer. It's trying to answer the commercial question sitting underneath them:
Signal 1A competitor appears in ChatGPT.
Signal 2A third-party list gets cited by Perplexity.
Signal 3Gemini understands the category but leaves your brand out.
Signal 4Claude describes the problem well, but learns the answer from a source you don't control.
Is this pattern strong enough to change what you do next?
The judgement layer behind the audit
AnswerPresence is run by So What Media.
The work sits at the overlap of content strategy, SEO, B2B buyer messaging, and AI-answer evidence. Reading one of those in isolation is how AEO advice ends up thin.
The combination changes which questions get asked.
A prompt that produces a striking AI result is not always a prompt a buyer would ask at a moment that matters. A source appearing frequently in AI answers is not always the source that moves a commercial decision. And a citation problem can get misread as a brand problem if the evidence is only read as visibility.
So the audit isn't just asking:
"Where do we appear in AI answers?"
It's asking:
"Which of these patterns should change what we do next?"
That's also why an AEO gap is rarely just one thing.
Sometimes it's a category-language problem. The AI tool doesn't understand what you do clearly enough.
Sometimes it's a brand-inclusion problem. The category is understood, but competitors are named and you aren't.
Sometimes it's a citation problem. AI tools are leaning on third-party sources, review pages, comparison lists, or articles that frame the category without you.
Sometimes it isn't a problem worth funding yet.
That last answer matters. An audit that can't say "not now" is just looking for a way to sell the next thing.
Otherwise it isn't an audit. It's a sales document in a jacket.
How we think about AEO evidence
The audit is built around a few constraints.
Their job is to stop the report becoming larger, cleaner, or more decisive than the evidence behind it.
A screenshot is a signal, not a decision
A screenshot proves something happened.
Once.
It doesn't prove the prompt is one a buyer would actually ask, that the pattern holds across tools, or that the answer goes the same way once named competitors are in the comparison.
The audit treats a screenshot as the start of an investigation, not the proof that funds one.
Signal, not proof
Tool readings need interpretation
Visibility data can show where your brand appears. It can't tell you which readings should change the plan.
A low score on a prompt no buyer would ask isn't the same as absence from a decision-stage answer.
Treating them as equal is how teams end up funding the wrong fix.
Reading, not plan
Claims need receipts
If the report says a competitor is being framed more clearly, the receipts should name the prompts, answers, mentions, sources, and URLs behind that claim.
No mystery layer.
No "trust us" box.
Receipts required
The recommendation should be narrow enough to use
The audit doesn't end with a general statement about creating more content.
It ends with one recommended next move: act on the findings internally, run a deeper diagnostic, scope a pilot, or do nothing for now.
A recommendation a team can't act on is an essay.
Narrow enough
Uncertainty should stay visible
Some readings are strong enough to act on. Some are directional. Some are interesting but not yet useful.
The audit marks the difference. Pretending all evidence carries the same weight is how teams end up chasing the wrong thing.
Evidence weighted
Discipline shows up in what's cut
If the audit gave every benchmark reading, every tool response, and every gap the same weight, it would be a data export, not a decision.
The discipline is keeping the report inside what the evidence shows, even when a louder version of the same finding would be easier to sell.
Not every prompt deserves the same weight. Not every missing mention deserves a workstream. Not every cited source is worth chasing. Not every worrying answer is strong enough to act on.
What remains is the pattern that should actually change what you do next.
The Method page covers how the audit is run. This page is about the judgement that decides what makes it into the report.
Prompts, answers, mentions, sources, and URLs behind that claim.
What we won't pretend
The audit is deliberately narrow.
That is not a limitation we're trying to hide.
It's the point of the offer.
That's less exciting than treating every AI-search gap as a new growth channel.
It's also more useful.
It isn't a full implementation plan dressed up as a cheap first step.
Before a company buys a tracking tool, briefs an agency, assigns internal work, or scopes a pilot, it needs a smaller decision: Do we have enough evidence to act?
It isn't continuous tracking.
The audit is deliberately narrow.
It isn't a technical SEO audit.
That is not a limitation we're trying to hide. It's the point of the offer.
It isn't a revenue forecast.
If the answer is yes, the audit says what to do next. If the answer is no, the audit should save you the larger spend. If the answer is "not yet," the audit says what would need to change.
The right call depends on the evidence
If you've already seen enough AI-answer evidence to be concerned, the next step isn't more screenshots.
Request the audit when you want a benchmark, the receipts, a walkthrough, and one evidence-backed recommendation on what to do next.
If you want to understand the process first, read the Method. It shows how the audit moves from category prompts to lost answers, evidence gaps, citation gaps, and the final recommendation.