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    Voice + Replay Feedback

    One embed captures voice, clicks, and scrolls. AI extracts tasks.

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    Client feedback without the detective workAI Task Extraction & Effort EstimatesCopy-Paste Prompts for Cursor & Claude CodeClient feedback without the detective workAI Task Extraction & Effort EstimatesCopy-Paste Prompts for Cursor & Claude CodeClient feedback without the detective workAI Task Extraction & Effort EstimatesCopy-Paste Prompts for Cursor & Claude CodeClient feedback without the detective workAI Task Extraction & Effort EstimatesCopy-Paste Prompts for Cursor & Claude Code
    Voice note in. Developer task out.

    The reviewer speaks.
    The task writes itself.

    GiveFeedback captures voice, visible screen state, interaction data, and page context — then structures it into a developer-ready task with replay evidence and targeted follow-up questions when something's ambiguous.

    How it works

    Every signal from the live session, fused into one developer-ready task.

    Replay intakeLive
    Browser session /pricing
    Visible state00:43

    "I like the layout, but this section feels overwhelming and I can't tell which plan is the default."

    1440px viewporthovered pricing cardspaused 5.2s
    Voice
    Intent, emotion, expected outcome
    Screen
    Viewport, visible UI state, current page
    Interaction
    Clicks, hovers, scroll depth, timing
    Context
    DOM references, metadata, task history
    Generated task
    Structured output, replay-linked
    92% confident
    Task
    Clarify the primary pricing plan and reduce visual noise in the comparison grid

    Reviewer hesitated on the pricing section, scanned across all three cards, and said the default option was unclear.

    Page: /pricingTimeline: 00:39–00:48Effort: 3 pts
    Task title and fix description tied to what was visible on screen
    Confidence score backed by replay, page, and interaction data
    Element-aware follow-up questions surfaced through the widget
    Approved tasks pushed to dashboard or used for prompt generation
    When confidence drops
    One precise question through the widget — not a vague ticket.

    "Which plan should feel recommended by default: Growth or Scale?"

    Why it works
    Speech becomes intent

    Emotional language is separated from the actual requested change — no interpretation needed on your end.

    Screen state locks context

    The spoken note is bound to exactly what was visible — not a memory of the page from three days later.

    Follow-up stays in context

    Clarification comes back through the widget, while the reviewer is still looking at the thing they flagged.

    Widget follow-up thread
    System asks

    Which kind of change are you asking for here?

    Option 1

    Do you want the pricing card to feel less busy or just shorter?

    Option 2

    Was the issue visible on desktop, mobile, or both?

    Option 3

    Which CTA should stay primary after the rewrite?

    Reviewer responds

    "Desktop and mobile. The problem is strongest on mobile because the cards stack and the CTA hierarchy disappears."

    What we actually analyze ↓

    Four signals.
    One task worth building.

    Transcription alone loses most of the signal. The visible page, cursor path, and hesitation timing are what turn a voice note into a task specific enough to ship without a follow-up thread.

    Voice

    Intent, emotion, expected outcome

    Screen

    Viewport, visible UI state, current page

    Interaction

    Clicks, hovers, scroll depth, timing

    Context

    DOM references, metadata, task history

    The outcome

    Fewer vague tickets. Fewer clarification loops. Better fixes on the first pass.

    Capture the full context, structure it automatically, and ask the one clarifying question before the reviewer closes the tab.

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