docs/upskilling/extras/upscaling_redundancy_breakdown.md

GrowQR — Upscaling Module — Redundancy vs Unique Work (Interview / Roleplay / Courses / Assessment)

Purpose: break down the 4 services in the Upscaling module, identify redundant modules we should share/standardize, and clarify what is unique per service.

Inputs reviewed (in growqr/plans/):

• interview service plan.md
• Roleplay_OnePager.docx
• Course_OnePager.docx
• Assessment_OnePager.docx

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1) Upscaling module: services and their primary job

1. Interview Service

• Real-time-ish interview practice, persona-driven Q/A
• Records user media + optional screen share
• Outputs 7-metric coaching + historical comparison

2. Roleplay Service

• Avatar-based AI roleplay (video/voice) + optional screen share
• Heavier focus on avatar, lip-sync, emotional expression
• Outputs weighted performance scoring + certificates for scenario thresholds

3. Courses Service

• Course catalog + external provider integrations
• Enrollment/progress tracking + certificates
• Recommendation engine consumes gaps from other services

4. Assessment Service

• Multi-stakeholder assessment marketplace
• Upload-first (CSV/JSON/Excel), AI-generated assessments as secondary
• Psychometric/behavioral data feeds the global recommendation engine

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2) Redundant modules (should be shared or standardized)

2.1 Identity / auth verification

All services require:

• verify user identity
• enforce org visibility (marketplace vs private library)

Recommendation: standard auth library / gateway policy + shared middleware.

2.2 Credits / entitlements

All services deduct credits:

• Interview: duration tier → credits
• Roleplay: per session
• Courses: per course type
• Assessment: possibly per attempt / premium

Recommendation: one central Credits/Entitlements service or shared library + consistent idempotent “deduct” contract.

2.3 Artifact storage + media broker

Interview/Roleplay generate large artifacts (video/audio/screen). Courses/Assessments generate smaller artifacts (certificates, uploads).

Shared needs:

• presigned upload/download
• artifact metadata registry
• retention policy and deletion

Recommendation: shared “Artifact Registry” module/service (even if storage is per-service).

2.4 Certificate generation

Courses, Assessments, Roleplay (and potentially Interview) all generate certificates.

Shared needs:

• template engine
• signing/verification
• storage + download links

Recommendation: shared Certificate service/library.

2.5 Eventing + outbox pattern

All services share data with each other and Dashboard.

Shared needs:

• event schema versioning
• retries, idempotency

Recommendation: standard event format + outbox implementation that every service uses.

2.6 Skill taxonomy + skill-gap representation

Recommendations depend on consistent “skills” and “gaps” representation.

Shared needs:

• canonical skill IDs
• mapping metrics → skills

Recommendation: define a shared Skill Taxonomy and a SkillGap schema used by all services.

2.7 Recommendation engine inputs/outputs

Courses are recommended based on:

• Interview weaknesses
• Roleplay weaknesses
• Assessment topic gaps + psychometrics

Shared needs:

• consistent “signals” API (what each service emits)

Recommendation: a shared “Recommendation Signals” contract:

• SignalProduced(service, userId, signalType, payload, timestamp, version)

2.8 Compliance baseline (US/EU/India)

All services must support:

• PII minimization
• retention controls
• deletion workflows
• access auditing

Recommendation: shared compliance checklist + shared primitives (retention tags, audit log schema).

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3) Unique modules (must be built per service)

3.1 Interview Service — unique work

• persona-driven interview state machine
• question bank and follow-up generation
• 7-metric interview-specific scoring weights
• historical comparison optimized for interview metrics

3.2 Roleplay Service — unique work

• avatar rendering integration strategy (real-time avatar vs pre-rendered video)
• voice synthesis + lip-sync quality targets (≤100ms target mentioned)
• scenario configuration and scenario-specific certificate rules
• roleplay-specific weights (Voice 35%, Facial 25%, Body 20%, Technical 10%, Content 10%)

3.3 Courses Service — unique work

• external provider integrations and progress sync/webhooks
• catalog ingestion + caching strategy
• enrollment state machine (in progress/completed/abandoned)

3.4 Assessment Service — unique work

• multi-stakeholder creator workflows
• bulk upload parsing + validation
• flexible data collection schema per creator
• timer/navigation engine
• creator analytics dashboards + exports
• psychometric/behavioral modeling signals

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4) Where client-side vs server-side makes sense (cross-service)

For Interview + Roleplay (real-time experiences)

Client-side is optimal for:

• recording + encoding
• optional local STT (for fast turn-taking)
• avatar rendering + lip sync
• optional MediaPipe feature extraction

Server-side is optimal for:

• session orchestration + credits
• question generation
• post-session scoring + report
• artifacts registry + retention/audit

For Courses + Assessment

Mostly server-side:

• workflows are CRUD + marketplace + sync
• no need for client-side heavy ML

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5) Recommended shared platform building blocks (minimal)

If we want “shared but not over-engineered”, build these as either shared libraries or small internal services:

1. Credits/Entitlements (idempotent deduction API)
2. Artifact Registry (metadata + presigned URL patterns)
3. Certificates (template + signing)
4. Event schemas + outbox (reliable cross-service sharing)
5. Skill taxonomy + SkillGap schema (powering recommendations)
6. Compliance primitives (retention tags, deletion requests, audit events)

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6) Quick risk notes

• If Interview and Roleplay choose different avatar/TTS strategies, they can still share:

  • the session + artifacts + scoring/report patterns
  • the eventing + skill gap outputs

• If we attempt server-side real-time audio/video streaming early, we significantly increase:

  • cost
  • compliance surface area
  • time-to-ship

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7) Next planning step

Decide and document:

1. The shared contracts (Credits, Artifacts, Events, SkillGap)
2. The client/server contract for Interview + Roleplay (what must client compute vs server)
3. The retention + deletion policy for recordings across markets