14. MVP vs Future Versions - Phased Rollout
Release Timeline
gantt
title MangaAssist Release Roadmap
dateFormat YYYY-MM
axisFormat %b %Y
section MVP (V1)
Design and Architecture :a1, 2026-03, 1M
Core Development :a2, after a1, 2M
Testing and QA :a3, after a2, 1M
Beta Launch (1% traffic) :milestone, m1, after a3, 0d
Gradual Rollout (100%) :a4, after a3, 1M
section V2
Post-Purchase Features :b1, after a4, 2M
Personalization :b2, after a4, 2M
V2 Launch :milestone, m2, after b2, 0d
section V3
Proactive Intelligence :c1, after b2, 3M
Multi-language :c2, after b2, 3M
V3 Launch :milestone, m3, after c2, 0d
MVP (V1) - Useful on Day One
Timeline: About 4 months to beta and 5 months to full rollout.
Goal: A chatbot that can answer the most common manga shopping questions and give basic recommendations.
What's Included
| Feature | Description | Why in MVP |
|---|---|---|
| Product Discovery | "Show me popular shonen manga" | Highest-value use case |
| Basic Recommendations | "Something like One Piece" using seed-based collaborative filtering | Core differentiator |
| FAQ / Policy Answers | Return policy, shipping info, payment questions via RAG | Deflects support tickets |
| Product Q&A | "Is this in English?" "How many pages?" via catalog lookup | Removes purchase friction |
| Human Escalation | "Talk to a human" with context handoff | Safety net |
| Chat Widget | Bottom-right FAB, basic chat panel, quick chips | The interface |
| Basic Guardrails | PII filter, toxicity filter, price validation | Non-negotiable for launch |
| Core Analytics | Session count, intent distribution, latency, error rate | Must measure from day one |
What's Not in MVP
- Order tracking
- Returns and refund flow
- Personalized greetings
- Proactive messages
- Cross-sell and upsell
- Multi-language support
- Mobile-specific optimizations
- A/B testing framework
MVP Architecture
graph LR
A[Chat Widget] --> B[API Gateway]
B --> C[Orchestrator]
C --> D[Intent Classifier<br>Rule-based only]
C --> E[RAG Pipeline]
C --> F[Recommendation Engine]
C --> G[Product Catalog]
E --> H[LLM - Bedrock]
F --> H
H --> I[Basic Guardrails]
I --> A
MVP uses rule-based intent classification only. That keeps the initial scope small and still covers the limited intent set.
V2 - Full Shopping Assistant
Timeline: About 2 to 3 months after MVP launch.
Goal: Handle the full shopping lifecycle including post-purchase support, and add personalization.
New Features
| Feature | Description | Why in V2 |
|---|---|---|
| Order Tracking | "Where is my order?" with real-time status | High-volume support case |
| Returns and Refunds | "I want to return this" with eligibility check | Reduces escalation |
| Promotion Awareness | "Any deals on manga?" | Drives incremental revenue |
| ML Intent Classifier | Fine-tuned BERT replacing rule-based routing | Higher accuracy as intent set grows |
| Personalized Greetings | "Welcome back! Vol 12 just released" | Increases engagement |
| Cart-Aware Responses | "The box set is cheaper than your 3 individual volumes" | Cross-sell opportunity |
| Enhanced Product Cards | Images, ratings, Prime badge, Add to Cart buttons | Richer UX |
| Feedback Loop | Thumbs up/down feeds prompt improvement | Continuous quality improvement |
V2 Architecture Additions
graph TD
subgraph "New in V2"
A[ML Intent Classifier<br>SageMaker]
B[Order Service Integration]
C[Returns Service Integration]
D[Promotions Service Integration]
E[User Profile Integration]
F[Feedback Pipeline<br>Kinesis to Model Improvement]
end
V3 - Proactive and Global
Timeline: About 3 to 4 months after V2.
Goal: Make the chatbot a proactive shopping advisor, support international users, and integrate more deeply with the store experience.
New Features
| Feature | Description | Why in V3 |
|---|---|---|
| Proactive Recommendations | Suggest titles based on browsing without being asked | Increases discovery |
| Cross-sell and Upsell | "Readers who bought this also loved..." at checkout | Lifts average order value |
| Multi-language Support | Japanese, Spanish, French, German | Serves international buyers |
| Voice Input | Speech-to-text for mobile users | Accessibility and convenience |
| Reading Order Guides | "What order should I read Fate/Stay Night?" | High-value content |
| New Release Alerts | Pre-order suggestions for upcoming volumes | Drives pre-order revenue |
| Wishlist Integration | "Add this to my wishlist" | Retains intent for future conversion |
| A/B Testing Framework | Test prompts, recommendations, and UX flows | Data-driven optimization |
| Advanced Analytics | Funnel analysis, cohort tracking, LTV impact | Long-term measurement |
V3 Architecture Additions
graph TD
subgraph "New in V3"
A[Proactive Trigger Engine<br>Event-driven suggestions]
B[Multi-language LLM<br>Prompt per locale]
C[Voice-to-Text<br>Amazon Transcribe]
D[A/B Testing Service<br>Feature flags + experiments]
E[Advanced Analytics<br>Cohort analysis in Redshift]
end
Evolution Summary
graph LR
V1[MVP<br>Discovery + FAQ<br>+ Recommendations<br>+ Escalation]
V2[V2<br>+ Orders + Returns<br>+ Personalization<br>+ ML Classifier]
V3[V3<br>+ Proactive AI<br>+ Multi-language<br>+ Voice + A/B Testing]
V1 -->|+3 months| V2 -->|+4 months| V3
| Dimension | MVP | V2 | V3 |
|---|---|---|---|
| Intents supported | 5 | 10 | 15+ |
| Data integrations | 3 | 7 | 10+ |
| Languages | English | English | English + Japanese + 3 more |
| Team size | 10-12 | 15-18 | 18-22 |
| Expected conversion lift | +2% | +5% | +8% |
| Support deflection | 30% | 50% | 70% |