# 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.