LifeOps Architecture
Enterprise-grade relationship intelligence platform built with Domain-Driven Design, Effect-TS, and science-backed psychology research.
Domain Map
High-level view of LifeOps bounded contexts and their interactions
Domain Types & Entities
Core type definitions and their relationships
Type Safety with Effect-TS
Effect.Effect<FineResponse, DecodeError, AnalysisService>Every operation explicitly declares its success type, error types, and dependencies.
Fine Decoder™ State Machine
How ambiguous messages are decoded through pattern matching and confidence calculation
RAG + Signals Draft Pipeline
How context-aware responses are generated using semantic search and behavioral signals
Why RAG + Signals?
Domain Event Flow
Event-driven architecture showing how domain events propagate through the system
Domain Events
- • MessageReceived - New message synced
- • SyncCompleted - Batch sync finished
- • ErrorDetected - Relationship error found
- • SignalsUpdated - Profile recalculated
Anti-Corruption Layer
WhatsApp protocol complexity isolated in Go binary. Domain code only sees clean JSON interfaces.
Roadmap & Phases
From foundation to full relationship intelligence platform
Effect-TS, DDD, WhatsApp Sync, Signal Extraction
@lifeops commands via WhatsApp
Four Horsemen, Magic Ratio, Bid Tracking
Love Languages, A.R.E. Score, Attachment Styles
Travel Safety, SOS Detection, Check-ins
Email, Calendar, SMS Integration
Science-Backed Foundation
Built on peer-reviewed relationship psychology research
Gottman Research
40+ years studying 3,000+ couples
- Four Horsemen detection (90% divorce prediction)
- 5:1 Magic Ratio tracking
- Bid response analysis
Attachment Theory
Explains ~80% of relationship patterns
- A.R.E. Framework (Accessible, Responsive, Engaged)
- Attachment style detection
- Anxious-Avoidant trap warning
Communication Science
NVC & conflict resolution research
- NVC translation (accusatory → constructive)
- Love language detection
- Apology language matching