Back to the Campfire Blog

AI Visibility Tools: Setting Expectations

Written by: Adrienne Kmetz Tags: technical-seo, data-pipelines, template

Published: Apr 10, 2026  |  Last Updated: Apr 16, 2026

The frustrating reality with measuring your brands' visibility in LLMs is that with hyper personalized results, the citation data changes with every prompt. And getting the volume you need to tease a trend out of the data, costs money. 

This article is a community-written guide pulling together hard-won lessons from practitioners working in AI search optimization and visibility measurement.

We'll cover how AI visibility tracking works, where it breaks down, how to DIY it, what tools exist, and what you should do regardless of the data you have.

AI Visibility is a measurement of how often your brand shows up in AI citations

At its core, AI visibility tracking means asking an AI model a question (or a series of questions) and recording how your brand shows up in the response. Simple enough in theory. But the moment you go deeper, things get complicated.

AI models don't return a static ranked list like a SERP. They generate an answer that is, to varying degrees, personalized to the user: their history, their location, the model version they're using, even whether they're on a free or paid plan.

There's a real philosophical tension here. The more "intelligent" and personalized an AI becomes, the less its outputs look like something you can track at scale.

That said, the community consensus is that tracking is still worth doing, you just have to do it carefully, be open about your assumptions, and understand what you're measuring and where it falls short.

Read Pedro's full article on how AI visibility works, how the bots work, and the underlying tech that you'll need to understand before moving forward on an AI data plan.

In other words: Your goal is to be so clearly associated with a topic or concept that the AI reaches for your brand naturally in case the user types a short keyword or has a long, context-rich conversation.

Limitations: Why the data is so messy

Before you invest heavily in AI visibility tracking, you need to understand the constraints. There are a lot of them.

The prompt problem

Your visibility scores are only as good as the prompts you're tracking. But prompts are different for everyone based on what they're looking for, and they tend to be longer, not shorter, than question-based keywords. Therefore tracking one prompt, or the wrong prompts, doesn't give you any data that you can make a decision on. 

You need to ask the same question many times

AI answers aren't deterministic. The same prompt can yield different results on consecutive runs, which means a single measurement gives you very little signal.

Free vs. paid accounts behave differently

Most tracking tools use free accounts to query AI platforms but free and paid users don't get the same experience. This is a significant gap.

Token limits on free accounts

Many AI platforms rate-limit the number of "deep thinking" queries free users can run. If your tracking tool depends on those, it may be systematically undercounting how often enhanced reasoning features are used in real answers.

Memory and personalization

Real users accumulate memory in their AI accounts. The AI learns their preferences, context, history, and surfaces answers accordingly. Tracking tools start fresh every time.

Location and locale

If you need country- or language-specific data, you need accounts tied to those locations. That's an additional operational and logistical burden that most tools haven't fully solved. AI visibility data is useful directional signal, not ground truth. Treat it the way you'd treat early organic search data in the late 2000s, that was worth tracking, worth acting on, but requiring a healthy dose of skepticism.

All of these limitations point to something deeper. Martin McGarry put it best:

Which is why the goal was never to find a perfect measurement system, it was to understand what you're measuring, and act accordingly.

What are you trying to track and why?

Victor put it well. When evaluating new AI tools, look at: 

DIY: Setting up your own tracking

Not ready to invest in a dedicated tool? You can get meaningful signal by doing this manually or with a lightweight setup. Here's how.

Step 1: Define your prompt set

Start with the questions your ideal customer is asking when they're close to a buying decision. These should map to your ICPs (Ideal Customer Profiles), not general industry queries. Think: "best [category] for [specific use case]" instead of  "what is [category]."

The closest we can get to simulating a real user right now is adding a role to the prompt, and Victor Pan explains why that matters:

In practice: if your ICP is a marketing director at a mid-size SaaS company, say so in the prompt. It won't perfectly replicate a real user's context, but it's the best signal we have right now.

A good starting prompt set has 20–50 queries spread across awareness, consideration, and decision-stage intent.

Step 2: Choose which AI platforms to track

At minimum: ChatGPT, Perplexity, and Gemini. Add Claude and Copilot if your audience skews enterprise or technical. Don't try to track everything at once it's better that you find depth on fewer platforms.

Step 3: Run each prompt multiple times

Following the guidance from the community: run each prompt at least 3 times, ideally on different days and at different times. Record whether your brand appears (yes/no), where in the answer, and how it's framed (positive, neutral, negative).

Some would argue you'd need to get to 150 runs in order to have a dataset that is reliable enough to make decisions on. Once you get past this part, you'll need to use a tool to get that kind of volume. 

Step 4: Log and track over time

A simple spreadsheet works. Columns: prompt, platform, date, brand mentioned (Y/N), mention type (top rec / list item / passing mention), sentiment. Review monthly for trends.

Step 5: Note what the AI says about you

Read the mentions. Is the AI describing your brand accurately? Is it citing the right use cases? Is it mentioning outdated pricing or discontinued features? This qualitative layer is often more actionable than the raw visibility number.

Evaluating tools

A growing category of AI visibility platforms has emerged to automate what we described above. The space is still maturing rapidly, capabilities and pricing change often, so verify current features directly with vendors.

When evaluating any AI tool, ask these questions:

• How many times does it run each prompt? What's the methodology for averaging?
• Does it track free or paid accounts? What model versions?
• Does it support multi-locale or multi-language tracking?
• How does it handle AI platforms that don't have public APIs (e.g., Perplexity)?
• What's the refresh cadence? Weekly? Daily? Real-time?

No tool currently solves all the limitations described in the previous section. Use them as directional dashboards, not authoritative measurement systems. 

On Reddit, user Real-Assist1833 shared their results from a ton of testing: 

So I tested ~20 tools over the last few months. Paid for most of them myself just to understand the differences.

Quick thoughts:

Enterprise stuff (Profound, Conductor, Semrush AI, AthenaHQ, etc.)
→ Very polished. Very expensive. Feels built for big marketing teams.

Mid-tier tools (AIclicks.ai, Peec, Writesonic, Surfer AI Tracker, Rankscale, Scrunch, Omnia, LLMClicks.ai, etc.)
→ More practical. Prompt tracking, brand mentions, share of voice across models.
Some give actual action steps. Some are just dashboards.

Indie tools (Otterly, Rank Prompt, Waikay, Passionfruit Labs, etc.)
→ More focused. Some track citations. Some check if AI is getting your facts wrong. Budget-friendly entry points.

What I learned:

• • No tool is “Search Console for AI” yet.

  • Results change depending on how you phrase the prompt.

  • API results and actual chat UI don’t always match.

  • Most tools are 70% similar.

The real value isn’t just “are we mentioned?”
It’s:

• • Why are we mentioned?

  • Which sources triggered it?

  • What does AI think our brand actually is?

Also… tools don’t fix weak positioning. Clear messaging + strong entity signals still matter more than dashboards.

View Reddit post

What to do regardless of the data

The best things you can do for AI visibility don't require perfect measurement. They're about making your brand easy for AI systems to understand, trust, and recommend.

Get your entity right

AI models work with entities, named things with defined attributes. Make sure your brand, products, and key people are clearly described and consistently represented across your website, PR, and third-party profiles.

Wikipedia entries, Wikidata, and authoritative directory listings all help. Before any of this works, AI systems need to be able to crawl your content in the first place. The Community has extensive thoughts about how AI bots behave with noindex and robots.txt.

Publish opinionated, specific content

Generic content doesn't give AI models anything to latch onto. The more specific and opinionated your content is about your use cases, your ideal customers, and your differentiation, the easier it is for AI to associate you with specific concepts.

Earn citations in trusted sources

AI models are grounded in web content and they favor sources they've learned to trust: major publications, established industry sites, academic papers. Getting mentioned and cited in those contexts matters more than publishing on your own domain.

Optimize for the concepts, not the prompts

As Mike Sonders noted: the goal is entity-concept association, not keyword ranking. Ask yourself: "When an AI thinks about [my category], do the signals it's learned clearly point to us?"

Build content and PR strategies that reinforce that connection. Think about what a knowledgeable friend would say about your brand in conversation. That's what you want AI to say. Write, publish, and earn coverage that makes that happen.

Monitor for accuracy, not just presence

Your AI visibility strategy should include a review process for what the AI actually says. Wrong pricing, outdated product names, misattributed claims. These are reputational risks that don't show up if you're only counting brand mentions.

Be patient and iterate

AI visibility is not a channel you can optimize in a sprint. It reflects the cumulative signal across your entire digital presence. Expect a minimum 3–6 month lag between content and PR efforts and measurable changes in how AI models talk about you.

The tools and methodologies are still catching up to the complexity of the problem. But that's not a reason to wait, it's a reason to build good habits now, before the space matures and your competitors figure it out.

Measure what you can. Act on what's directionally clear. And stay close to communities where practitioners are sharing what works.