Veribix vs Profound

What Profound measures

Profound sends prompts to answer engines and records how a brand appears in the replies. Its published plans are drawn along how many engines you reach and how many prompts you track: the Starter plan covers ChatGPT alone with fifty prompts, Growth covers three engines with a hundred, and Enterprise extends to as many as nine. The unit being sold is coverage of the model layer.

On top of that it offers agents — autonomous workers with a monthly credit allowance — aimed at acting on what the monitoring finds. That is a content-optimisation motion built on the same polled data.

What Veribix measures

Veribix starts from the other end. It reads your own Google Analytics property, your Search Console data, your server logs and, where you proxy through Cloudflare, your crawler activity. From those it assembles a funnel per assistant and per page: crawled, clicked, converted, revenue. Those are your numbers, in your systems, and you can check any figure against the redacted log line or the analytics row it came from.

Veribix also polls model answers, because a company that is never named cannot be clicked. But the polling is the second pillar, not the product. The first pillar is what actually arrived, and the two are reported separately so you can see whether visibility turned into anything.

What neither can see

Before comparing features it is worth naming the limits both approaches share, because a vendor that does not name them is not being straight with you.

Referrer stripping is the first. A growing share of assistants send outbound links with the referring page removed, so a real visit that began in an answer lands in analytics as direct traffic. Profound cannot see it because it does not read your analytics; Veribix reads your analytics and still cannot recover the link, which is why it reports the attributable portion and states the rest separately rather than folding an estimate into a headline.

The second is the mention that produced no click. If an assistant names you and the reader simply remembers it, that influence is real and unmeasurable. Answer polling detects the mention and cannot tell you whether anyone acted; first-party data detects the action and cannot see the mention behind it. Running both narrows the gap without closing it, and neither vendor should pretend otherwise.

The third is variance. Ask the same question twice and the companies named change. Any single reading is a sample. That is why Veribix reports the share of runs in which a company appeared with the sample count shown, and refuses to compress it into one figure implying a settled order.