Profound vs Scrunch AI — and How Botfusions Beats Both

Profound tracks eight answer engines, rising to nine on Enterprise: Perplexity, ChatGPT, Claude, Gemini, Grok, Microsoft Copilot, DeepSeek and Google AI Overviews. Scrunch AI tracks four on Core — ChatGPT, Perplexity, Google AI Overviews and Copilot — and nine on Enterprise, adding Claude, Gemini, Meta AI, Google AI Mode and Grok. The overlap is large; the differences are at the edges. Profound reaches DeepSeek, Scrunch AI Enterprise reaches Meta AI and Google AI Mode. Which set is better depends entirely on where your buyers actually ask.

On price the two enter the market differently. Profound starts at $99 per month, but that tier covers ChatGPT alone with 50 prompts. Its three-engine Growth tier is $399. Scrunch AI starts higher at $250 per month, and that Core plan covers four engines with 125 prompts; its nine-engine coverage is on the Enterprise plan, priced on request. So the cheaper headline number is Profound's.

Prompt volume is where they are hardest to compare directly. Profound counts analysed responses — 1,500 on Starter, 9,000 on Growth. Scrunch AI counts prompts — 125 on Core. Both are proxies for the same thing: how much of your category the measurement actually samples. Neither number is meaningful without knowing how many distinct questions your buyers ask.

Comparing these two platforms against each other is a reasonable exercise, and if you have decided you want a self-serve dashboard, coverage and price will settle it. But the framing quietly assumes the answer to a prior question: that measurement is what you are missing.

For a team with SEO, content and engineering capacity already allocated, that assumption holds. The dashboard closes a genuine gap and the work flows from it. For a team without that capacity, it does not hold — and the failure mode is predictable. The subscription runs, the prioritised list grows, the backlog absorbs it, and two quarters later the score is where it started. The conclusion drawn is usually that the category does not work, when what happened is that nothing shipped between measurements.

So before comparing engine counts, it is worth answering plainly: in the next ninety days, who writes the schema, who publishes and maintains llms.txt, who rewrites the service pages so the answers are extractable, who builds the citation sources you do not own? If those names exist and have time, buy the platform that covers your engines at the best price. If they do not, the gap is execution, and no amount of additional measurement closes it.