kalei/docs/build-phases/phase-4-spectrum-and-scale.md

Phase 4 - Spectrum and Scale

Duration: 3-6 weeks Primary owner: Data + backend + product analytics

1. Objective

Deliver Phase 2 intelligence features and scaling maturity:

• Spectrum weekly and monthly insights
• aggregated analytics model over user activity
• asynchronous jobs and batch processing
• cost, reliability, and scaling controls for growth

2. Entry Criteria

Phase 3 exit checklist complete.

3. Scope

3.1 Spectrum Data Foundation

Implement tables and data flow for:

• session-level emotional vectors
• turn-level impact analysis
• weekly aggregates
• monthly aggregates

Data design requirements:

• user-level partition/index strategy for query speed
• clear retention and deletion behavior
• exclusion flags so users can omit sessions from analysis

3.2 Aggregation Pipeline

Build asynchronous jobs:

1. post-session analysis job
2. weekly aggregation job
3. monthly narrative job

Job engineering requirements:

• idempotency keys
• retry with backoff
• dead-letter queue for failures
• metrics for queue depth and job duration

3.3 Spectrum Insight Generation

Implement AI-assisted summary generation using aggregated data only.

Rules:

• do not include raw user text in generated insights by default
• validate output tone and safety constraints
• version prompts and track prompt revisions

3.4 Spectrum API and Client

Backend endpoints:

• weekly insight feed
• monthly deep dive
• spectrum reset
• exclusions management

Mobile screens:

• emotional landscape view
• pattern distribution view
• insight feed cards
• monthly summary panel

3.5 Growth-Ready Scale Controls

Implement scale milestones:

• worker isolation from interactive API if needed
• database optimization and index tuning
• caching strategy for read-heavy insight endpoints
• cost-aware model routing for non-critical generation

4. Detailed Execution Plan

Week 1:

• schema rollout for spectrum tables
• event ingestion hooks from Mirror/Turn/Lens

Week 2:

• implement post-session analysis and weekly aggregation jobs
• add metrics and retries

Week 3:

• implement monthly aggregation and narrative generation
• implement spectrum API endpoints

Week 4:

• mobile spectrum dashboard v1
• push notification hooks for weekly summaries

Week 5-6 (as needed):

• performance tuning
• scale and cost optimization
• UX polish for insight comprehension

5. Quality and Analytics Requirements

Quality gates:

• no raw-content leakage in Spectrum UI
• weekly job completion SLA met
• dashboard load times within agreed target

Analytics requirements:

• track spectrum engagement events
• track conversion impact from spectrum teaser to upgrade
• track retention lift for spectrum users vs non-spectrum users

6. Deliverables

Functional deliverables:

• Spectrum dashboard v1
• weekly and monthly insight generation
• user controls for exclusions and reset

Engineering deliverables:

• robust worker pipeline with retries and DLQ
• aggregated analytics tables with indexing strategy
• end-to-end observability for job health and costs

7. Exit Criteria

You can exit Phase 4 when:

• weekly and monthly insights run on schedule reliably
• users can view, reset, and control analysis scope
• spectrum cost and performance stay inside defined envelopes
• data deletion behavior is verified for raw and derived records

8. Risks To Watch

• Risk: analytics pipeline complexity causes reliability issues.
  • Mitigation: isolate workers and enforce idempotent jobs.
• Risk: insight quality is too generic.
  • Mitigation: prompt iteration with rubric scoring and blinded review.
• Risk: costs drift with growing history windows.
  • Mitigation: aggregate-first processing and strict feature budget controls.