WhatsApp Catch-Up Product Sense Flow Map
Rectangular boxes and directional arrows mapping users, problems, feature logic, metrics, and risks.
All
Pain → Solution
Primary Flow
AI Logic
Trust
Metrics
Core User Problem
Users receive long voice notes and miss important decisions, questions, and action items.
User Segments
Busy Professionals
Need fast triage between meetings and deadlines.
Wedding Planners
Coordinate vendors, family, payments, RSVPs, and decisions.
Family Coordinators
Track travel, pickup, hotel, and care logistics.
Students
Catch up on group projects, class notes, and deadlines.
Group-Chat-Heavy Users
Need signal from noisy multi-person voice-note threads.
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Pain Points
Long Voice Notes
18-minute clusters are hard to listen through.
Hidden Asks
Questions are buried inside casual audio.
Scattered Decisions
Payments, dates, approvals, and choices get lost.
Reply Friction
User knows what to say but has to reconstruct context.
Trust Risk
Audio is private and summary mistakes can cause harm.
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Jobs To Be Done
Skim Fast
Know what happened without listening to every second.
Find My Asks
See exactly where someone needs input.
Capture Decisions
Turn audio into structured decisions and action items.
Reply Confidently
Draft a useful response without losing the chat context.
Stay In Control
Review, edit, consent, and override AI output.
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Core Feature
Inline Catch-Up Summary
Long voice notes → concise summary and topics.
Needs Your Input
Hidden asks → explicit questions needing response.
Action Items
Scattered decisions → checklist of things to do.
Suggested Replies
Reply friction → editable draft responses.
Global Catch-Up Inbox
Multiple chats → one queue of voice-note follow-ups.
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AI Logic
Transcription
Convert voice notes into searchable text segments.
Silence Skipping
Remove greetings, pauses, and low-signal audio spans.
Topic Clustering
Group segments into venue, payment, RSVP, logistics, etc.
Action Extraction
Detect commitments, decisions, owners, and deadlines.
Question Detection
Identify asks directed at the user.
Suggested Reply Generation
Create short, warm, detailed, or point-by-point drafts.
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Metrics
Voice Note Completion Rate
More users finish or confidently skip long audio.
Time Saved
Minutes avoided per voice-note cluster.
Action Item Response Rate
Questions and decisions get answered faster.
Catch-Up Usage Rate
Users open summaries when voice notes are long or clustered.
Reply Conversion Rate
Inserted AI drafts become sent replies.
Primary User Flow
Open WhatsApp
User enters normal chat surface.
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Select Unread Voice Note Chat
Ellie or another voice-heavy thread.
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Tap Catch Up
Open inline summary or global Catch-Up inbox.
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Review Summary
See topics, decisions, transcript, and important parts.
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Answer Questions
Select asks and insert suggested reply.
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Mark Items Done
Sent reply updates answered state and reduces backlog.
Trust, Privacy, and Consent Requirements
End-to-End Encryption
Summaries must not weaken WhatsApp’s privacy promise.
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On-Device Where Possible
Prefer local transcription and extraction for sensitive audio.
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Clear User Consent
User opts into summaries and understands AI limitations.
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Review Before Send
Suggested replies are never auto-sent.
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Traceability
Questions link back to transcript and voice-note segment.
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AI Disclaimer
Summaries may be wrong; original audio remains available.
Tradeoffs and Risks
Accuracy vs Speed
Fast summaries are useful, but errors can mislead.
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Privacy vs Intelligence
Richer AI may require more context and stronger safeguards.
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Automation vs User Control
Drafts should help, not make decisions for the user.
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False Positives
Action detection may invent tasks or over-prioritize casual comments.
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Tone Mismatch
AI replies can sound colder, warmer, or more decisive than intended.
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Over-Notification
Too many Catch-Up prompts can become another inbox.