Do LLMs already know your brand? A five-step audit for AI visibility
If your team has started pasting AI answers into Slack, you’re not alone.
Someone asks a basic question about your company, and the response is close, but not quite. It gets the category mostly right, but misses nuance. It pulls an old detail. It compares you to a competitor you barely see in deals.
Then it turns into a lot of motion and not much signal. You make updates, but you can’t tie them to any outcome you’d show a leadership team.
Start with a baseline instead—something you can rerun, compare, and turn into a plan.
1) Capture a baseline before you change anything
Pick a small prompt set you can repeat monthly. Six is enough.
Use real buying questions:
What is [brand]?
Who is [brand] for?
What does [brand] do best?
Compare [brand] vs [competitor]
Best alternatives to [brand]
What should someone consider before choosing [category]?
Save the answers as they appear. Log the date. Pull out the claims that repeat. Also note who shows up alongside you.
Expect variation. Most prompts fan out into sub-questions behind the scenes, so you’re looking for patterns, not perfection.
What to capture in your audit doc
When you run the prompts, don’t just save the answers. Save the context around them. Otherwise you’ll rerun this next month and forget what you were reacting to.
A simple table is enough:
Prompt: the exact wording you used
Answer summary: one sentence on what it claimed about you
What felt off: wrong category, dated detail, generic positioning, competitor mismatch
Confidence: did it sound certain, hedged, or inconsistent?
Source signal: did it cite anything, name a site, or feel like it pulled from reviews/forums?
Impact: would this confuse a buyer, create an objection, or just be mildly annoying?
That last column is the one most teams skip. “Mildly annoying” isn’t where you start. Start with anything that mispositions you, creates friction for sales, or changes who you get compared to.
2) Run an identity check (entity clarity)
If answers are fuzzy, it’s often because the system isn’t clear on who you are.
Write a one-page “entity card”:
Official name (plus common variations)
Category and subcategory in buyer language
What you sell in one sentence
Who it is for
The differentiator you want repeated
Two proof points you can defend
Then compare it to what you captured. If you sound generic, the category drifts, or old positioning keeps resurfacing, fix those signals first.
And don’t skip basics: core SEO foundations still matter. If the site is hard to crawl or messy, you’re making everything downstream harder.
3) Decide what showing up actually means
Many teams say they want visibility. That sounds clear until you try to measure it.
Because showing up can mean a few different things, and they don’t behave the same way. You can show up more often and still have the wrong story told about you. You can be cited as a source and still not be the recommended option. You can even get recommended, but for the wrong reason, which creates a whole different kind of friction.
So before you change anything else, decide which version of showing up you actually care about right now.
Here are the three outcomes worth separating:
Mention: You appear in the answer, but lightly. Maybe it’s a list of options. Maybe it’s one sentence. Mentions are a sign you are in the conversation, not that you are shaping it. If you’re missing entirely for category questions or competitor comparisons, getting to consistent mentions can be real progress.
Citation: Your site or content is doing work in the answer. The assistant leans on your language, your definitions, your comparison framing, or your explanation of the category. This is usually where structure starts to matter a lot. Clear section headings, tight definitions, and pages that answer a question directly make it easier for systems to pull you in as an input, not just name-drop you.
Recommendation: The system points the user toward you. This is where positioning is make-or-break. Recommendations tend to happen when the assistant can explain who you are for, what you’re best at, and how you’re different without guessing. It’s also where being recommended for the wrong reason is dangerous. If you keep getting positioned as a generic tool, a budget option, or an enterprise-only platform when that isn’t true, you’ll feel it downstream in lead quality and sales cycles.
Once you separate these, the work gets simpler. If your priority is mentions, you’re trying to make sure you belong in the set. If your priority is citations, you’re trying to become a reliable source. If your priority is recommendations, you’re trying to make your differentiation easy to repeat accurately.
Pick one as your primary goal for the next cycle of work. Otherwise, you’ll end up doing a little of everything, measuring nothing cleanly, and still feeling unsure whether progress is happening.
4) Read the demand trail, not just referral traffic
Check AI referral traffic where you can. Look at landing pages and what visitors do next.
But don’t judge success by referral volume. These are answer-first experiences.
Two signals often matter more:
Branded search trendlines
Direct traffic trendlines
Keep tracking strategically, too. Focus on prompts tied to buying decisions and comparisons, not every top-of-funnel curiosity.
5) Identify what shapes the answers, then pick one lever
If your site isn’t being pulled in, something else is shaping the narrative. Often it’s reviews, directories, forums, YouTube, competitor comparisons, or older pages that keep getting repeated.
Don’t chase everything. Start with leverage.
First, tighten the pages that define you:
a plain-language “what we do” page
audience or use case pages
capability pages that remove ambiguity
one or two comparison pages that answer the question directly
Structure matters. These systems pull chunks. Clear headings and direct definitions help.
Next, fix anything that creates pipeline friction. If AI repeatedly frames you as “enterprise-only,” “expensive,” or “the same as X,” that becomes a sales objection.
Finally, add something first-party: benchmarks, surveys, internal insights, and real frameworks. Generic category content blends in—specific, owned detail sticks.
What you should have when you’re done
You’re done when you have:
a baseline snapshot you can rerun
the few patterns that matter most
a short list for the next 30 days
Then rerun the same prompts next month and compare what changed.
Do you want a second set of eyes on the audit?
If your team is feeling pressure to “do something with AI,” this gives you a clean starting point and a way to show progress without pretending every win turns into a click.
Kinetic can help you run the audit, pinpoint what is shaping the narrative, and focus the next 30 days on the highest-leverage fixes.
