159 lines
3.7 KiB
Markdown
159 lines
3.7 KiB
Markdown
# Phase 4 - Spectrum and Scale
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Duration: 3-6 weeks
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Primary owner: Data + backend + product analytics
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## 1. Objective
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Deliver Phase 2 intelligence features and scaling maturity:
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- Spectrum weekly and monthly insights
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- aggregated analytics model over user activity
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- asynchronous jobs and batch processing
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- cost, reliability, and scaling controls for growth
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## 2. Entry Criteria
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Phase 3 exit checklist complete.
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## 3. Scope
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## 3.1 Spectrum Data Foundation
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Implement tables and data flow for:
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- session-level emotional vectors
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- turn-level impact analysis
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- weekly aggregates
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- monthly aggregates
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Data design requirements:
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- user-level partition/index strategy for query speed
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- clear retention and deletion behavior
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- exclusion flags so users can omit sessions from analysis
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## 3.2 Aggregation Pipeline
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Build asynchronous jobs:
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1. post-session analysis job
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2. weekly aggregation job
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3. monthly narrative job
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Job engineering requirements:
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- idempotency keys
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- retry with backoff
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- dead-letter queue for failures
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- metrics for queue depth and job duration
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## 3.3 Spectrum Insight Generation
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Implement AI-assisted summary generation using aggregated data only.
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Rules:
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- do not include raw user text in generated insights by default
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- validate output tone and safety constraints
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- version prompts and track prompt revisions
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## 3.4 Spectrum API and Client
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Backend endpoints:
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- weekly insight feed
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- monthly deep dive
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- spectrum reset
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- exclusions management
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Mobile screens:
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- emotional landscape view
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- pattern distribution view
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- insight feed cards
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- monthly summary panel
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## 3.5 Growth-Ready Scale Controls
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Implement scale milestones:
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- worker isolation from interactive API if needed
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- database optimization and index tuning
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- caching strategy for read-heavy insight endpoints
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- cost-aware model routing for non-critical generation
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## 4. Detailed Execution Plan
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Week 1:
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- schema rollout for spectrum tables
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- event ingestion hooks from Mirror/Turn/Lens
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Week 2:
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- implement post-session analysis and weekly aggregation jobs
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- add metrics and retries
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Week 3:
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- implement monthly aggregation and narrative generation
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- implement spectrum API endpoints
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Week 4:
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- mobile spectrum dashboard v1
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- push notification hooks for weekly summaries
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Week 5-6 (as needed):
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- performance tuning
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- scale and cost optimization
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- UX polish for insight comprehension
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## 5. Quality and Analytics Requirements
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Quality gates:
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- no raw-content leakage in Spectrum UI
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- weekly job completion SLA met
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- dashboard load times within agreed target
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Analytics requirements:
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- track spectrum engagement events
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- track conversion impact from spectrum teaser to upgrade
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- track retention lift for spectrum users vs non-spectrum users
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## 6. Deliverables
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Functional deliverables:
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- Spectrum dashboard v1
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- weekly and monthly insight generation
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- user controls for exclusions and reset
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Engineering deliverables:
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- robust worker pipeline with retries and DLQ
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- aggregated analytics tables with indexing strategy
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- end-to-end observability for job health and costs
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## 7. Exit Criteria
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You can exit Phase 4 when:
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- weekly and monthly insights run on schedule reliably
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- users can view, reset, and control analysis scope
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- spectrum cost and performance stay inside defined envelopes
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- data deletion behavior is verified for raw and derived records
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## 8. Risks To Watch
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- Risk: analytics pipeline complexity causes reliability issues.
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- Mitigation: isolate workers and enforce idempotent jobs.
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- Risk: insight quality is too generic.
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- Mitigation: prompt iteration with rubric scoring and blinded review.
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- Risk: costs drift with growing history windows.
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- Mitigation: aggregate-first processing and strict feature budget controls.
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