Keep the data. Change what happens next.
Session replay is useful evidence, but replaying sessions manually is a slow way to decide what deserves a fix. Dibzzy adds a layer that finds repeated friction and turns it into a product decision.
01
Tools such as Hotjar, Microsoft Clarity, PostHog, and FullStory help teams see clicks, scrolls, errors, and individual journeys. That visibility matters. The problem starts when a team has more recordings than time to review them.
02
Dibzzy watches for repeated behaviour across sessions and applies evidence gates before an AI explanation is written. The result is not a prettier replay inbox; it is a shorter path from signal to a defensible decision.
03
A replay tool is built for exploration: open a session, inspect the timeline, and form a hypothesis. Dibzzy is built for diagnosis: collect the repeated signal, explain the likely cause, and prepare the next action.
Most product teams need both modes at different moments. The right question is not whether one tool has more charts; it is whether the team can consistently get from visitor behaviour to a shipped improvement.
04
Dibzzy is intended to layer onto the data you already collect where possible. Start with one important flow, review the evidence-backed issues, and decide whether the output saves your team enough manual review to keep using it.
Straight answers
No. Dibzzy is designed to add diagnosis and prioritization after visitor data exists. Your replay tool can remain useful for context and investigation.
Dibzzy does not treat an AI explanation as proof. A finding must first clear evidence gates around affected sessions, rates, and repeated elements; only then does AI help explain the likely cause.
Yes. The public Dibzzy overview includes an illustrative issue, and the demo lets you see the product workflow before deciding what to connect.
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