Watches everything, continuously
Dibzzy reads every click, scroll, and hesitation across your real visitor sessions — using your existing analytics data or its own lightweight capture.
Evidence-first UX
Your analytics tool shows you the data. Dibzzy shows you the fix.
You're already paying for something that records every visitor session. Dibzzy watches it for you, proves where people struggle, then hands your team a diagnosis, mockup, and ticket — ready to ship.
Issue evidence / live
Medium confidence130 sessions · last 7 days
Nobody knows what to type in “Workspace URL” — so they guess, fail, and drop out.
Evidence
Errors on this one field61
Retries after the error44
Signups abandoned here17
Proposed design
Create your workspace
Workspace URL — lowercase letters and dashes, e.g. acme
acme
Your team will be at acme.dibzzy.app
Checked as you typeContinue →
17 abandoned signups in 7 days — mockup + ticket ready after approval
You already have the data. Nobody has the time.
Somewhere in your stack, a tool records exactly where visitors get stuck, confused, or give up. Between "we should dig into that" and actually doing it, weeks go by.
Dibzzy is the part of the process your team doesn't have hours for.
How it works
From raw sessions to a shipped fix, without manual review.
Every stage narrows the signal. AI explains proven patterns; it does not decide what counts as evidence.
Proves the pattern is real
Issues clear evidence gates first: enough affected sessions, a rate above your baseline, and the same element repeated.
Explains it, in plain language
Only then does AI name the cause, connect it to your goals, and explain why it matters.
Hands you the fix
Approve an issue to get a proposed design, a before-and-after mockup, and an engineering ticket.
Example issue · illustrative
Here's what an issue looks like.
Medium confidence
“Nobody knows what to type in ‘Workspace URL’ — so they guess, fail, and drop out.”
61 validation errors on a single field and 44 retries across 130 sessions in 7 days. 17 people abandoned signup at this step.
What's happening
The field is labelled “Workspace URL” with no example and no hint about what's allowed. People type a company name with spaces, hit Continue, and only then see “Invalid value” — with no explanation of the rule. Some retry twice and leave.
What it's costing
17 abandoned signups in a week — around 70 a month. On a $49 plan, that's roughly USD 400–1,000 of lost signups each month — assuming a fix recovers 10–30% of affected visits. Illustrative estimate, not a customer result.
Current screen
Create your workspace
Workspace URL
Acme Corp Ltd
Invalid value
The rule only appears after it's broken.
Proposed design
Create your workspace
Workspace URL — lowercase letters and dashes, e.g. acme
acme
Your team will be at acme.dibzzy.app
Checked as you typeContinue →
- Show a real example in the field
- Explain the rule before the error
- Validate while typing, not on submit
Before/after mockup + ready-to-copy ticketGenerated automatically after approval
Stop reviewing sessions.
The review already happened. Dibzzy hands you the findings that cleared the gate.
Get a fix, not a finding.
Every issue comes with a cause, a proposed design, and a ticket — not another replay to interpret.
Know what it's worth.
Each issue carries an estimated cost so you can prioritise by business impact, not gut feel.
Why Dibzzy
The difference isn't more data. It's what happens after.
Evidence-gated, not guessed.
Findings clear deterministic thresholds before AI explains them. A hallucination cannot talk its way past a gate it never reaches.
A finished decision, not a data point.
Every issue comes with a plain-language cause, a proposed design, and a ticket — not another replay to interpret.
Honest about uncertainty.
Low-confidence findings stay visibly labelled. Your team decides what is worth acting on.
Straight answers
Questions worth asking.
We already have Hotjar, Clarity, PostHog, or FullStory. Why add Dibzzy?
Those tools show what happened. Dibzzy determines whether it is a repeated pattern worth fixing and hands your team the fix, rather than another observation to review.
How do we know a confident finding is actually right?
Every issue must clear evidence gates — minimum affected sessions, a rate above your site's baseline, and the same element repeated — before AI explains it. Confidence stays visible, including when it is low.
Will this become another vendor to manage?
Dibzzy layers onto the data you already collect where possible, rather than asking you to replace your stack. Its job is to remove manual review, not create more of it.
Stop watching replays. Start shipping fixes.
Connect your site in a few minutes and see your first evidence-backed issue within days, not weeks.