iMediate WhatsApp
WhatsApp bot parsing threads for NVC guidance and court-ready exports.
Problem Defined
"Unstructured chat lacks the legal foundation required for conflict management."
Strategic Context
Parents default to WhatsApp but lose legal defensibility.
Competitive Imbalance
Standard messaging leads to information overload and drift.
System Hypothesis
Injecting structure into existing social graphs maximizes adoption.
Process Architecture
How the system was designed, tested, and refined.
DEFINE
Inject legal structure into existing WhatsApp social graphs.
- • Audited parental chat behaviors on social apps
- • Identified legal export gaps
- • Mapped coordination drift
- • Focused on standalone app adoption, ignored existing habit pull
- • High-stress users won't switch apps; the tool must go to them
- • Pivoted to a WhatsApp-native bot interface
MAP
Map chat parsing logic to court-ready documentation.
- • Mapped NLU intent to NVC protocols
- • Created calendar parsing diagrams
- • Identified audit log nodes
- • Initial parsing was too broad, capturing irrelevant domestic noise
- • Structure must be surgical to preserve privacy and relevance
- • Created intent-based filtering for legal vs personal chat
VALIDATE
Test adoption and export validity in chat workflows.
- • Ran pilot with 20 WhatsApp users
- • Measured bot response adherence
- • Validating exports with legal counsel
- • Users found bot interruptions intrusive in group chats
- • Bot presence must be requested, not forced
- • Switched to reactive command structure
EXECUTE
Build the WhatsApp parsing and export engine.
- • WhatsApp bot layer
- • NLU intent engine
- • Legal export system
- • Over-engineered the parsing for complex image data
- • Textual integrity and intent are the primary legal requirements
- • Focused on message intent and timestamp integrity
MEASURE
Calculate adoption rate and record recoverability.
- • Cohort retention
- • Protocol adherence
- • Export validity
- • Vanity metrics on message volume obscured actual de-escalation
- • Volume reduction per dispute is the true success indicator
- • Refined KPIs to focus on dispute cycle duration
Rule Application
How doctrine was operationalized.
Intellectual Rigor
01_INT- • Mapping social graph before bot deployment
- • Defining legal audit requirements
100% record recoverability achieved in legal trials
Tactical Execution
02_TAC- • Deploying on users' primary channel
- • Short iteration on NLU intents
Bot live and parsing in 2 weeks
Human Calibration
03_HUM- • Reducing barrier to entry for legal logging
- • Preserving natural chat flow
90% adoption rate in resistant user cohorts
Machine Leverage
04_AI- • Using NLU for relational conflict detection
- • Automated export synthesis
AI handles semantic parsing without human oversight
Product Architecture
WhatsApp bot, calendar parsing, NVC reframing, audit logs.
AI Leverage
Natural Language Understanding for relational conflict.
Outcomes & Learnings
Meeting users in their primary channel increased adherence.
