Product truth, without the theatre
Dibzzy exists for the gap between collecting visitor behaviour and making a product decision. Its job is to make repeated friction easier to inspect, explain, and hand off without pretending uncertainty has disappeared.
01
A finding should begin with observable behaviour: repeated errors, retries, hesitation, dead clicks, or abandonment around a meaningful journey step. AI can help explain a pattern after the evidence is assembled; it should not invent the pattern.
02
The product is designed to take an approved issue toward a proposed design, before-and-after mockup, and implementation-ready ticket. The output is a starting point for a team review, not an automatic answer.
03
Dibzzy is not a general analytics replacement, a session-video viewer, an experimentation platform, a guarantee of conversion uplift, or a substitute for qualitative research and domain expertise. Public examples are illustrative unless explicitly identified otherwise.
04
Choose one journey where your team already has a question and enough data to inspect. Review whether Dibzzy makes the evidence clearer and the handoff more useful; then validate the released change in the systems you already trust.
Straight answers
This page intentionally describes the product and its boundaries rather than inventing team biographies, customer logos, or traction claims that are not established in the public product material.
No. Public examples and teardown scenarios are illustrative unless explicitly presented as verified customer evidence, and this site does not currently make that customer-result claim.
Trust it to structure an evidence-led workflow and produce a reviewable proposed next step. Do not treat it as a guarantee of causality, uplift, or a replacement for judgement.
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