Scrunch AI is a platform that tracks whether AI names your brand

Scrunch AI is an AI-visibility, or AI-search-monitoring, platform. It tracks whether your brand shows up in the answers that ChatGPT, Google AI Overviews, and other models give to the questions your buyers ask, whether you are named in those answers or merely cited underneath them, which competitors get named instead, and which sources the answer was built from. It is a rank tracker for the AI era, except the thing it watches is not your position on a page, it is whether you exist in the answer at all.

Here is the verdict up front, because you came for one. Scrunch is worth it if you are serious about generative engine optimization and you will act on what it shows you. But the tool is not the moat, and the dashboard is not the work. The work is the prompt library you point it at, and most of this review is about getting that right, because that is the part that actually decides whether the tool is worth anything to you.

What Scrunch tracks: named versus cited, per prompt, across every AI platform

The core job is answering a few questions on a schedule, at the level of each individual prompt:

  • Are you named, or just cited? A model can pull from a listicle that happens to mention you and still name three competitors as the recommendation. Scrunch reports named-versus-cited down to the specific prompt, so you see not just that you appeared, but whether you were the answer or a footnote. That is the whole difference between a citation and a mention, measured per query.
  • Visibility versus citation. It splits the two, and this matters more than it sounds. Being visible and named in the answer beats being a buried citation every time. A citation is worthless if your brand is not the thing the reader actually walks away with. We already know this, but it is good to see it separated in the data.
  • Who gets named instead of you, and from where. It shows the competitors the model recommends and the sources it synthesized, so you know both the share you are losing and where the off-site work has to happen.

It tracks essentially every AI platform: ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity, Grok, even Meta. You choose which ones to watch, and you should. I only track the platforms my buyers actually use. If your audience is not on Grok, do not waste a tracked prompt watching it. Scrunch runs each prompt roughly every other day and logs the response, so you get a real trend instead of a one-time snapshot, and I keep the whole set deliberately bottom-of-funnel.

How to build your Scrunch prompt library

This is the part that makes or breaks the tool, and it is the first thing I do on a new account after we have identified the target money keywords. The prompt library is not a keyword list. It is a deliberate map of every way a real buyer might ask an AI tool about the problem you solve. I build it from a combination of sources:

  1. Start from your money keywords. The bottom-of-funnel commercial terms you already locked in seed the prompts closest to a decision.
  2. Add the products, use cases, and terms the client wants watched. Every account has specific products and use cases they want to know people are looking for. Layer those in, because a generic keyword list will miss them.
  3. Turn persona pain points and jobs-to-be-done into prompts. For each buyer persona, take their actual pain points and the jobs they are trying to get done, and phrase them the way that person would type them into ChatGPT. Those persona questions become your long-tail prompts.
  4. Keep it bottom-of-funnel and buyer-real. Favor the decision-stage questions a real buyer asks, and pick the platforms they actually use.
  5. Cap the set: cover everything once, then stop. This is the trap people fall into. They build way too many prompts, and then it is impossible to show growth on any of them, impossible to optimize for them, and eventually you are staring at a list where you do not even know what half of them are for.

You are only as good as the prompts you track. Not the most prompts. The right ones.

The goal is a healthy, bounded set: enough to cover your entire range of personas, use cases, and product portfolio, and not one prompt more. On one HR-tech account I grew the tracked set from 139 to 200 prompts, and it worked because every addition was deliberate, the CFO-ready ROI questions, the manager-enablement queries, the high-intent industry cuts, the competitive “vs” terms, the “alternatives to [competitor]” switcher wedge, the modern-integration questions. That is expansion with a reason. Doubling a list just to make the number bigger is how you end up managing noise.

How I read Scrunch and tie it to pipeline

Once the library is right, the reading is simple: watch named-versus-cited move over time, per prompt and in aggregate, note which competitors are taking the answers you want, and see which sources the models keep pulling from. Then tie the movement back to influenced pipeline the way I do with everything else worth measuring. A brand-mention trend that never connects to a demo or an opportunity is just another vanity chart. The point of tracking named-versus-cited on real buying prompts is that it is a leading indicator for the pipeline that shows up later.

What Scrunch showed me: your own site is a sliver of what AI cites

The most useful thing this kind of tracking ever told me was uncomfortable. I pulled the citation export across six B2B SaaS accounts, every domain an AI answer cited across a large tracked prompt set, and aggregated it. Of all the sources cited about a brand’s own category, the brand’s own website was about 2.6%. Third-party sites were roughly 88.7%, and competitor domains about 8.7%. On average a company’s own site was only the 3.7th most-cited source about its own category, and in the cybersecurity account it ranked ninth.

You cannot see that without a tool pointed at the right prompts, and you cannot fix it by editing your own pages, because your own pages are 2.6% of the input. That single readout reframes the whole job as off-site, which is the argument I make in full in the GEO statistics data drop and in off-page SEO. Scrunch is how I know it is true for a specific account instead of true in general.

Where Scrunch falls short: the honest gripes

No tool review is worth reading without the negatives, and this category has real ones.

The suggestions are not useful. Scrunch will try to hand you little lists of things to do to improve your showing, and honestly, I have not found them worth much. The value is in the raw named-versus-cited data and the sources, not in the tool’s advice about what to do with it. That part you still bring yourself.

You add competitors manually, and the labeling gets messy. You enter your competitors by hand, which means you can only track so many, and in a crowded category that is a real ceiling. The knock-on effect is that things get labeled third-party when they are really competitors you did not or could not add. It is partly a byproduct of industries that have dozens upon dozens of competitors, but it means you should read the third-party share with a little skepticism rather than as gospel.

The big one, and it is not Scrunch’s fault: there is no volume data on prompts. This is a limitation of every AI platform right now, not this tool specifically. We have no real search volume around these prompts. None of the platforms expose how often a given question is actually asked. That is a serious gap, because without volume you are optimizing for questions you cannot confirm anyone is asking.

My workaround, and my honest sequencing, is this. I tie prompts back to real keywords wherever I can, so I am anchored to demand I can actually verify. And I usually gear prompt-level optimization toward a bit later in the strategy, because I would rather first optimize for keywords I know people are searching for. There is almost always lower-hanging fruit than chasing prompts with no confirmable demand behind them. I am not saying I do not use Scrunch, and I am not saying it does not work, we have seen real results from it. I am saying that until these companies expose volume, probably when they start selling ads inside their platforms, prompt tracking is a powerful but partially blind instrument, and you should treat it that way.

Optimizing prompts with no volume data is optimizing in the dark. Tie them to keywords you can verify, and you at least have a candle.

Is Scrunch AI worth it? Who it is for and who it is not

Worth it, with a condition. Scrunch is for teams that treat being named in AI answers as a real channel, will build a disciplined prompt library, and will act on the readout: run digital PR toward the sources that feed the answers, and report the movement as pipeline. For that team it is one of the few tools that measures the thing now deciding B2B discovery.

It is not for a team that wants a set-and-forget score, and it is not the place to start if you have not done the keyword-level work yet. If you are earlier than that, Google Search Console’s AI-impression report and a handful of manual prompt checks in ChatGPT will tell you roughly where you stand for free. Graduate to a tracker when you are ready to manage the number, not just look at it, and when your prompt library will be built with intent instead of thrown together.

Scrunch AI vs Profound: the honest positioning

I will not fake a head-to-head I have not earned. I run Scrunch. Profound is the other serious tool people put in the same category, and it is the main alternative you will weigh, but I have not spent enough hands-on time in it to give you a fair feature-by-feature verdict, and you should distrust anyone who claims a confident one without the reps. What I can tell you is that the category matters more than the logo. Both are trying to answer the same question, are you the named answer for the prompts your buyers ask, and the differentiator in your results will be your prompt library and your off-site work far more than the specific tool. Pick one, point it at real buying questions, and act on it.

Scrunch AI FAQ

How do you build a prompt library for AI-visibility tracking?

Build it from four sources, then cap it. Start with your money keywords, add the specific products and use cases the client wants watched, and turn each buyer persona’s pain points and jobs-to-be-done into prompts phrased the way that person would actually ask an AI tool. Keep everything bottom-of-funnel and buyer-real. Then stop once you have covered the full range of personas, use cases, and products, because tracking too many prompts makes it impossible to show growth, optimize, or even remember what each one is for. You are only as good as the prompts you track, not the number of them.

Which AI platforms does Scrunch track?

Scrunch tracks essentially all of them: ChatGPT, Google AI Overviews, Gemini, Claude, Perplexity, Grok, and Meta, and you choose which to watch. It runs each prompt roughly every other day and logs the response so you get a trend, not a snapshot. Track the platforms your buyers actually use and skip the rest rather than spreading a fixed prompt budget across surfaces your audience is not on.

How much does Scrunch AI cost?

Pricing for AI-visibility tools in this category is generally quote-based rather than a public flat rate, and it changes, so pull the current plans from the vendor directly instead of trusting a number in a review. The more useful framing: the cost that matters is not the subscription, it is whether you have someone who will build a disciplined prompt library and act on the output. A tracker you look at and ignore is expensive at any price.

What are the best Scrunch AI alternatives?

Profound is the alternative most teams weigh against Scrunch in the AI-visibility-tracking category. Beyond a dedicated tracker, the free starting point is Google Search Console’s Generative AI performance report plus manual prompt checks in ChatGPT, Perplexity, and Google AI Overviews. For how these fit alongside the rest of a real stack, see the AI SEO tools I actually use and the SEMrush vs Ahrefs breakdown.