Recall vs Canonry

Recall measures how ChatGPT, Copilot, Perplexity, Gemini, and Google AI Overviews answer the questions your buyers ask, and keeps the evidence: the stored answer behind every number, the pages it cited, and which of the four causes of an absence applies. Then it measures whether the fix worked. Canonry is an open-source tool covering the same engines.

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Frequently Asked Questions

What is the difference between Recall and Canonry?

Canonry is an open-source tool. Recall is an answer-engine measurement platform: it runs your questions repeatedly, stores every answer and the pages each one cited, and reports a share of answers with the confidence interval around it. Where you are missing, it names the cause — the engines cannot read your pages, cannot tell who you are, are not reading sources that mention you, or describe you in the wrong terms — because each of those has a different fix.

What do you get with Recall that a visibility score does not give you?

The working behind it. Every figure opens onto the answers it was computed from, with the cited pages captured at the moment they were cited. Recommendations are tied to a named cause rather than a lower score, and once you ship one, Recall re-measures the same questions and reports whether the answer actually changed — including when the result is inconclusive.

Is Canonry open source?

Yes, Canonry is open source, so you can read and run the code yourself. Recall is a closed-source hosted platform: what it opens up is the evidence rather than the source — every number links to the stored answer and cited pages it came from.

Can I self-host Recall?

Not at the moment. Recall runs as a hosted platform, with each customer's data isolated at the database level. Measuring answer engines means paying for every answer collected, which is why it ships as a service rather than something you run yourself.