Echelon Integration Plan

Echelon Integration Plan

---
⚠️ FUTURE VISION DOCUMENT

Status: 🔮 Planned Q1-Q2 2026 - This document describes a planned integration with Echelon (Computational Choreography engine).

Current Reality: The `echelon-bridge` service (`:3005`) is a stub returning mock data. No actual Echelon integration exists yet. This document preserves the architectural vision for future implementation.

---

Overview

This document outlines how Echelon's embodied dynamics engine will integrate with TrajectoryOS to provide unparalleled life-trajectory intelligence through motion-to-music computational choreography.

Integration Architecture

Data Flow

Echelon Engine (Real-time)
    ↓ (embodied signals: 100Hz movement, rhythm, tension)
Echelon Bridge
    ↓ (session summaries: aggregated metrics)
Trajectory Core
    ↓ (life physics updates: alignment, gravity, skill confidence)
User Dashboard

Signal Mapping

Echelon's embodied variables map to TrajectoryOS life variables:

Session-Level Aggregation

Echelon outputs high-frequency signals. The bridge aggregates these into session summaries:

interface EchelonSessionSummary {
  sessionId: string;
  userId: string;
  startTime: Date;
  endTime: Date;

  // Aggregated metrics
  avgTension: number;
  avgDrift: number;
  avgMomentum: number;
  avgPhase: number;
  flowScore: number; // % of time in flow state

  // Detected regimes
  flowEpisodes: Array<{ start: Date; end: Date }>;
  breakdownPeriods: Array<{ start: Date; end: Date }>;

  // Raw latent vector for advanced analysis
  latentVector?: number[];
}

Integration Points

1. Real-Time Stream (Future)

Echelon → WebSocket → Echelon Bridge → Event Bus → Trajectory Core

• Bridge subscribes to Echelon's real-time output
• Buffers signals into sliding windows
• Emits session summaries on session end or every N minutes

2. Batch Analysis (Future)

Stored Echelon Session Data → Bridge Analyzer → Trajectory Core Updates

• Reprocess historical movement data to refine life state estimates
• Used during onboarding to bootstrap user's trajectory model

3. Contextual Tagging (Critical)

Each Echelon session must be tagged with context:

{
  sessionId: "...",
  context: {
    activity: "dance_practice" | "dj_set" | "work" | "conversation",
    projectIds: ["bwb-id", "cc-core-id"],
    skillIds: ["choreography", "mixing"],
    location?: string,
    notes?: string
  }
}

This allows TrajectoryOS to attribute embodied signals to specific life domains.

The Moat: Why This Is Unreplicable

1. Hidden Modality: Competitors can't observe embodied dynamics; they only have text and calendar data
2. Years of Research: Echelon embodies years of choreographic and computational research that can't be copied
3. Unique Insight: Only TrajectoryOS can detect:
- When verbal claims contradict embodied reality
- Flow states that indicate true alignment
- Micro-tensions that precede burnout
- Rhythm coupling that signals skill mastery

Implementation Phases

### Phase 1: Core Life Physics (Current)
- Build TrajectoryOS without embodied signals
- Use only conversation + artifacts
- Prove the physics model works

### Phase 2: Echelon Integration
- Connect Echelon bridge to real Echelon engine
- Stream embodied signals during sessions
- Fuse embodied + verbal evidence

### Phase 3: Recursive Intelligence
- TrajectoryOS recommends movement practices to improve alignment
- Echelon detects when recommendations are followed
- Closed-loop system for life optimization

Technical Requirements for Phase 2

• [ ] Echelon Engine exposing WebSocket or gRPC API
• [ ] Session context tagging in Echelon
• [ ] Real-time signal aggregation in bridge
• [ ] Event bus for async updates to Trajectory Core
• [ ] Python models updated to consume embodied features
• [ ] Dashboard visualizations for embodied signals

Open Questions

1. How to handle sessions with ambiguous context (e.g., commute with music)?
2. Privacy controls around storing raw movement data?
3. Should embodied signals override verbal claims, or only flag contradictions?

---

Status: Awaiting Echelon maturity. Bridge service ready to activate when Echelon API is stable.