AI chatbots like ChatGPT, Claude, and Perplexity are now where people go to ask for recommendations instead of Googling.
If your brand doesn’t show up in those answers, you’re invisible at the exact moment people are ready to buy.

In this guide, we’ll break down what to monitor to understand how visible your brand really is in LLMs — from first impressions to final conversions. Then, at the end, we’ll show you a cost-effective way to track it all automatically.

What to monitor

1. Track impressions: when LLMs see you 👀

Before you ever get a visit or a lead, an AI model has to see your site.

That happens when AI crawlers from ChatGPT or Perplexity access your content to answer user questions.

Every time that happens, it’s an AI search impression — your brand is being read, cited, or mentioned.

Why it matters

If your pages are being fetched regularly, you’re part of the AI discovery ecosystem.

If not, you’re not even in the running.

The caveats

• A crawl doesn’t always mean your content appeared in an answer (over-reporting).

• Some AIs pull from cached data or pre-trained content, which doesn’t trigger a crawl (under-reporting).

• You can’t know where your content appeared in the answer — top or buried.

Still, impressions are your best indicator of AI visibility trends: are you more or less visible in LLMs?

2. Track visits: when users click-through 🖱️

When your brand is mentioned in an LLM answer and a user clicks to visit your website, that’s an AI search visit — the most visible signal of real discovery.

You can spot these in Google Analytics (GA4) by filtering the source e.g. source contains chatgpt

The caveat: massive under-reporting

Most LLMs mentions don’t include clickable links, so when users discover your brand in an answer - what do they do next?

They guess the website URL by typing directly in the browser OR search your brand on Google and click on it in the SERP.

In both cases, most anlaytics platforms (e.g. Google Analytics) attributes the visit to direct or organic, not chatgpt.

That’s why the real traffic from ChatGPT is often 10x higher than what shows up in analytics.

Even so, tracking the few visible visits you do get is still valuable — they show you are getting traffic and what are your most discoverable pages.

3. Track leads: when users convert 📝

Once someone signs up / books a demo / buys your product, you want to know if they first discovered you through an AI tool.

Analytics can’t tell you that yet — so the only reliable way is self-attribution.

Add a short “How did you hear about us?” question to your signup form. Include “ChatGPT / AI search” as one of the options.

That single field will reveal how much of your pipeline originates from AI mentions — and you’ll likely find it’s much higher than what analytics tools suggest.

4. Track share of voice: when users search your service 🏅

You can’t improve what you don’t monitor.

The final piece is knowing which prompts in LLMs actually trigger your brand mentions.

There are four prompt categories worth tracking:

1. High-intent prompts — “best [category] tool,” “alternatives to [competitor]” - 20 to 50 prompts

2. Competitor alternatives — prompts that compare or replace another product - 10 to 30 prompts

3. Brand sentiment — “is [your brand] legit,” “why is [brand] popular” - 5-10 prompts

4. Popular industry questions — general questions your audience asks - 50 to 200 prompts

By tracking these prompts, you’ll see:

• How often you brand appears

• Which competitors dominate the conversation

• Whether your brand is mentioned at all (and where in the answer e.g. first brand mentioned?)

• Whether your content is cited as a source

If you are in multiple markets, you should track each separately e.g. UK share of voice, USA share of voice.

That’s your AI share of voice.

The caveat: LLMs are non-deterministic

They don’t always give the same answer twice — even for the exact same question.

That means your brand might appear in one response but not in another, even seconds apart.

How to get around it:

• Track enough prompts (not just 5 or 10) to smooth out randomness.

• Regular rerun — daily, weekly or monthly — to see consistent trends rather than one-off spikes.

You’re not measuring a fixed ranking like in SEO; you’re tracking your visibility probability — how often your brand surfaces across enough prompts and enough run.

How to monitor your LLM visibility cost-effectively

Here’s the problem: manually checking LLM answers and crawling server logs is a nightmare.

Most AEO/GEO/AI Search analytics tools charge $250–$1000/month to monitor 50+ prompts.

There’s a cheaper, easier way.

Airefs lets you monitor:

• AI search impressions (when LLM crawlers access your content)

• AI search visits (when users click your brand from LLMs)

• AI share of voice(how often your brand appears across ChatGPT)

And it’s simple to set up:

1. Create an account

2. Add up to 250 prompts -> share of voice.

3. Integrate crawler analytics -> AI search impressions & visits.

Within 24 hours, you'll track impressions, clicks, mentions and citations - all for just $50/month (that’s 5–10× cheaper than other AI visibility tools).

Final Thought

The LLM era has its own visibility funnel — impressions, visits, and leads — but it’s still invisible to most brands.
Start tracking all three. Once you know where you stand, improving visibility becomes a data problem, not a guessing game.

And if you want it done without hiring a data team, Airefs can set it all up for free — you’ll only pay when you want to scale beyond the basics.