FlickPick: AI-Powered Movie Assistant - Design Document

Date: November 18, 2025
Status: Approved Design
Category: Mobile Application - Entertainment

Executive Summary

FlickPick is an AI-powered mobile application that solves "entertainment decision fatigue" - the daily micro-problem where people spend 20+ minutes scrolling through Netflix without deciding what to watch. The app uses conversational AI to understand user's current mood and context, then provides curated recommendations from personalized categories.

Key Differentiators:

Chat-based interface with minimalist AI assistant
Context-aware recommendations (mood, energy level, time of day)
Passive learning (minimal user input required)
Optional social layer (friend recommendations without noise)
Platform-agnostic (shows where content is available)

1. Problem Statement

Core Problem

People experience decision fatigue when choosing what to watch. With thousands of options across multiple streaming platforms, the paradox of choice leads to:

20+ minutes of scrolling without decision
Random selection out of frustration
Giving up and watching nothing
Repeated watching of familiar content (comfort zone)

Target Users

Age: 25-45
Behavior: Regular streaming service users (2+ platforms)
Pain Point: "I want to relax but can't decide what to watch"
Context: Evening relaxation time, limited mental energy

Why Current Solutions Fail

Netflix/Prime algorithms: Generic "recommended for you" without context
JustWatch/Reelgood: Search tools, not decision assistants
Letterboxd: Social discovery but requires active browsing
TasteDive/Likewise: Recommendation engines but lack context awareness

2. Solution Overview

Product Vision

FlickPick is a personal entertainment assistant that eliminates micro-decisions through AI-powered, context-aware recommendations.

Value Proposition

❌ Netflix: 10,000 options → ✅ FlickPick: 15 relevant suggestions
❌ Generic algorithms → ✅ Context-aware (mood + taste + friends)
❌ Complex setup → ✅ 30-second onboarding, learns as you use

Core Features (MVP)

Chat Interface: Quick conversation to capture current mood/context
Curated Categories: 4 personalized categories with 3-4 recommendations each
Passive Learning: Simple "Watched it / Skipped it" feedback
Social Recommendations: Optional friend influence (privacy-first)
Platform Information: Shows which streaming service has the content

3. User Experience Flow

First-Time Onboarding (30 seconds)

1. Welcome screen: "Find movies without deciding"
2. Platform selection: "Which streaming platforms do you have?"
   [✓] Netflix  [✓] Prime Video  [✓] Disney+  [ ] Others
3. Done → Ready to use

Daily Usage Flow

User Opens App
    ↓
AI Chat: "What's your vibe today?"
    ↓
User: "Tired, need something light"
(or quick tap: 😴 Tired | 😊 Good | 🔥 Energetic)
    ↓
AI Presents Categories:
┌──────────────────────────┐
│ TODAY'S PICKS            │
│ [4 movie posters]        │
├──────────────────────────┤
│ LIGHT & RELAXING         │
│ [4 movie posters]        │
├──────────────────────────┤
│ FRIENDS LOVED THESE      │
│ [3 movie posters]        │
├──────────────────────────┤
│ NEW DISCOVERIES          │
│ [4 movie posters]        │
└──────────────────────────┘
    ↓
User Taps Movie → Detail View
    ↓
Views: Poster, synopsis, rating, platform
Taps: "Note to Watch" or closes
    ↓
Later: Opens app
"Did you watch X?" → ✓ Watched | ✗ Skipped
    ↓
AI Learns → Better recommendations next time

AI Chat Characteristics

Tone: Minimalist and efficient
Style: Short, clear sentences without fluff
Example: "Vibe: Tired → Suggesting light content"
No: Excessive emojis, verbose explanations, unnecessary chat

4. Technical Architecture

Technology Stack

Frontend (Mobile):

Framework: React Native + Expo
State Management: Zustand (lightweight)
UI Library: Native Base or Tamagui
Navigation: React Navigation
Platform: iOS + Android (single codebase)

Backend:

Runtime: Node.js + Express.js
Database: PostgreSQL (user data, history, preferences)
Cache: Redis (sessions, content cache)
API Style: REST API (GraphQL optional for future)

AI Layer:

Chat Interface: OpenAI GPT-4 API
Context Understanding: GPT-4 for mood/preference extraction
Recommendations: Custom algorithm + GPT-4 assist
Future: Fine-tuned model or open-source LLM (cost optimization)

External APIs:

TMDb API: Movie/TV metadata, posters, ratings
JustWatch API: Platform availability information
Future: Streaming platform APIs (if available)

System Architecture

┌─────────────────────────────────────┐
│   MOBILE APP (React Native)         │
│                                     │
│  ├─ Chat Interface (UI)             │
│  ├─ Content Grid (Categories)       │
│  ├─ Detail View                     │
│  └─ Local State (Zustand)           │
└──────────────┬──────────────────────┘
               │ REST API
               ↓
┌─────────────────────────────────────┐
│   BACKEND (Node.js + Express)       │
│                                     │
│  ├─ User Service                    │
│  ├─ Recommendation Engine           │
│  ├─ Content Service                 │
│  └─ Social Service                  │
└──────────────┬──────────────────────┘
               │
     ┌─────────┴──────────┬─────────────┐
     ↓                    ↓             ↓
┌──────────┐    ┌───────────────┐  ┌─────────┐
│PostgreSQL│    │AI Service     │  │ Redis   │
│          │    │(OpenAI GPT-4) │  │ Cache   │
│User Data │    │+ Custom       │  │         │
│History   │    │Recommendation │  │Sessions │
│Feedback  │    │Algorithm      │  │Content  │
└──────────┘    └───────┬───────┘  └─────────┘
                        ↓
              ┌─────────────────┐
              │External APIs    │
              │                 │
              │- TMDb (Content) │
              │- JustWatch      │
              │  (Platforms)    │
              └─────────────────┘

5. Data Models

User Model

{
  id: UUID,
  name: string,
  email: string,
  platforms: ['netflix', 'prime', 'disney+'],
  createdAt: timestamp,
  tasteProfile: {
    preferredGenres: Map<genre, weight>,
    avoidedGenres: Map<genre, weight>,
    preferredLanguages: ['tr', 'en'],
    averageRating: float,
    watchTimePreference: 'short' | 'medium' | 'long'
  }
}

WatchHistory Model

{
  id: UUID,
  userId: UUID,
  contentId: string, // TMDb ID
  contentType: 'movie' | 'series',
  recommendedBy: 'ai' | 'friend' | 'browse',
  watched: boolean,
  watchedAt: timestamp,
  context: {
    mood: string, // Extracted from chat
    timeOfDay: string,
    dayOfWeek: string
  }
}

SocialConnection Model

{
  userId: UUID,
  friendId: UUID,
  status: 'pending' | 'active',
  createdAt: timestamp
}

Content Cache (Redis)

{
  tmdbId: string,
  title: string,
  poster: url,
  genres: array,
  rating: float,
  platforms: ['netflix', 'prime'],
  synopsis: string,
  runtime: minutes,
  cachedAt: timestamp
}

6. AI Recommendation Engine

Recommendation Algorithm Flow

1. CONTEXT CAPTURE (Chat Phase)
   Input: User chat message or emoji selection
   Process: OpenAI GPT-4 extracts structured context
   Output: Context object
   
   Extracted Data:
   - Mood: 'tired', 'energetic', 'sad', 'bored', 'excited'
   - Preference hints: 'light', 'deep', 'comedy', 'action'
   - Available time: 'quick' (<60min), 'normal', 'binge'

2. TASTE PROFILE LOOKUP
   - Load user's historical preferences from database
   - Calculate genre weights from past watches
   - Identify temporal patterns:
     * Time of day preferences
     * Day of week patterns (e.g., Friday = comedy)
     * Seasonal trends

3. SOCIAL LAYER (if enabled)
   - Query friends' recent watches (last 30 days)
   - Filter for high-rated content (friend rating > 4/5)
   - Calculate friend similarity scores
   - Weight by friend-user taste overlap

4. CONTENT FILTERING
   TMDb Query Parameters:
   - Genres matching mood + historical preferences
   - Available on user's selected platforms (JustWatch)
   - Minimum rating threshold (IMDB > 6.5)
   - Release window: Last 5 years (bias toward fresh content)
   - Language: User's preferred languages

5. SCORING & RANKING
   For each content item, calculate:
   
   total_score = 
     (0.40 × tasteMatch) +        // Genre/actor/director preferences
     (0.30 × contextMatch) +       // Current mood/energy alignment
     (0.20 × socialBoost) +        // Friends' ratings & watches
     (0.10 × popularityScore)      // General quality (IMDB/RT)
   
   Where:
   - tasteMatch: Cosine similarity to user's genre preferences
   - contextMatch: Mood alignment (e.g., tired → light genres)
   - socialBoost: Average friend rating × similarity weight
   - popularityScore: Normalized IMDB rating

6. CATEGORIZATION
   Create 4 categories:
   
   - "Today's Picks": Top 4 by total_score
   - "Light & Relaxing": Filter mood='tired', genres=['comedy', 'feel-good']
   - "Friends Loved These": Filter socialBoost > 0.5, top 3
   - "New Discoveries": Recent releases, high tasteMatch, not yet watched

7. RETURN
   4 categories × 3-4 items = ~15 total recommendations
   Format: {categoryName, items: [{tmdbId, title, poster, platforms}]}

Learning Loop

Positive Feedback (Watched):

Genre weight +0.1 for all genres in content
Actor/director weight +0.05
Context pattern recorded (time, day, mood)
Friend similarity +0.02 if recommended by friend

Negative Feedback (Skipped):

Genre weight -0.05
If skipped 3+ times in same category, reduce weight -0.1

Pattern Detection:

Weekly analysis of watch patterns
"User watches comedy on Fridays" → boost comedy on Fridays
"User avoids long movies on weekdays" → filter runtime

Cold Start Strategy:

First 5 recommendations: Popular + platform trending
Optional quick taste test: Show 6 posters, "Like or Pass?"
If friends connected: Immediately use their data

7. Social Features & Privacy

Social System (Optional)

Friend Connection Methods:

Phone contact sync (with permission)
Username search
QR code sharing
Mutual approval required (no auto-follow)

How Social Influence Works:

Scenario 1: One-Way Connection (Pending)

You add friend, they haven't accepted yet
You see nothing from them
No data shared

Scenario 2: Mutual Friends (Active)

Both users accepted connection
Passive visibility: "Friend X watched this and loved it"
No feed, no timeline, no notifications
Only appears in "Friends Loved These" category
Privacy: Friend can disable sharing in settings

Scenario 3: Social Disabled

Settings → "Social Features: Off"
AI ignores all friend data
Purely personal recommendations

Privacy & Data Security

Data Collection:

Watch history (local + server sync)
Chat conversations (for AI learning)
Platform preferences
Social connections (optional)
Anonymous usage analytics

Data Usage:

Personal recommendation improvement only
NO third-party sales
Anonymous crash reports and usage patterns
OpenAI API: Chat context sent (disclosed in privacy policy)

User Controls:

"Clear History" button
"Social Features: Off" toggle
"Delete Account" (GDPR/KVKK compliant)
Export data request (GDPR compliance)

Transparency:

Privacy summary on first launch
"AI is learning" indicator
Friend recommendations labeled: "X loved this"
Clear data retention policy (2 years, then anonymized)

8. UI/UX Design

Design Principles

Minimalist: Clean, uncluttered, focus on content
Dark Mode First: Evening usage, eye-friendly
Fast: Every interaction <200ms response
Accessible: Screen reader, high contrast, font scaling

Color Palette

Primary: Deep purple/blue gradient (#4A148C → #1565C0)
Accent: Soft yellow/orange (#FFA726) for highlights
Background: Dark gray (#121212) with subtle gradients
Text: White (#FFFFFF) and light gray (#E0E0E0)

Key Screens

1. Chat Screen (Entry Point)

┌────────────────────────┐
│  🎬 FlickPick          │
├────────────────────────┤
│                        │
│  AI: "What's your      │
│      vibe today?"      │
│                        │
│  [____________]        │ (Text input)
│                        │
│  Quick Pick:           │
│  [😴] [😊] [🔥] [😐]   │
│                        │
│  [Send]                │
└────────────────────────┘

2. Categories Screen (Main Content)

┌────────────────────────┐
│ ← Today: Tired         │ (Breadcrumb)
├────────────────────────┤
│ TODAY'S PICKS         │
│ ┌──┐┌──┐┌──┐┌──┐     │ (Poster grid)
│ │  ││  ││  ││  │     │ Horizontal scroll
│ └──┘└──┘└──┘└──┘     │
├────────────────────────┤
│ LIGHT & RELAXING       │
│ ┌──┐┌──┐┌──┐┌──┐     │
│ │  ││  ││  ││  │     │
│ └──┘└──┘└──┘└──┘     │
├────────────────────────┤
│ FRIENDS LOVED THESE    │
│ ┌──┐┌──┐┌──┐          │
│ │👤││👤││👤│          │ (Friend avatar overlay)
│ └──┘└──┘└──┘          │
├────────────────────────┤
│ NEW DISCOVERIES        │
│ ┌──┐┌──┐┌──┐┌──┐     │
│ │  ││  ││  ││  │     │
│ └──┘└──┘└──┘└──┘     │
└────────────────────────┘

3. Detail Screen

┌────────────────────────┐
│ ← Back                 │
├────────────────────────┤
│                        │
│   [LARGE POSTER]       │
│                        │
├────────────────────────┤
│ Movie Title            │
│ ⭐ 8.2/10 • 2h 15m    │
│                        │
│ [N] Available on       │
│     Netflix            │
│                        │
│ Synopsis:              │
│ Lorem ipsum dolor...   │
│                        │
│ Genre: Drama, Thriller │
│                        │
│ ┌────────────────────┐ │
│ │ Note to Watch  🔖  │ │ (Primary action)
│ └────────────────────┘ │
│                        │
│ 💬 "Your friend Ali    │
│    loved this"         │
└────────────────────────┘

4. Profile/Settings

┌────────────────────────┐
│ Profile                │
├────────────────────────┤
│ My Platforms           │
│ › Netflix, Prime       │
│   [Edit]               │
│                        │
│ Social Features        │
│ › Friends (3)          │
│ › [Toggle] ✓ Enabled   │
│                        │
│ Watch History          │
│ › 47 movies watched    │
│   [View All]           │
│                        │
│ Privacy                │
│ › Clear History        │
│ › Export Data          │
│ › Delete Account       │
└────────────────────────┘

Interactions & Animations

Smooth scrolling: 60fps category scrolling
Poster hover: Gentle zoom (1.05x) + soft shadow
Chat typing: Animated dots while AI processes
Swipe gestures: Right swipe → detail, left swipe → dismiss
Pull to refresh: Categories update with new context

Accessibility Features

Screen reader support (VoiceOver/TalkBack)
High contrast mode option
Adjustable font sizes (small/medium/large)
Voice input for chat (speech-to-text)
Haptic feedback for key actions

9. MVP Development Plan

MVP Scope (3-4 Months)

Phase 1: Core Foundation (4-6 weeks)

✅ Backend API setup (Node.js + Express + PostgreSQL)
✅ User authentication (email/password + OAuth)
✅ TMDb API integration
✅ OpenAI API integration (chat)
✅ Basic recommendation algorithm (genre-based)
✅ Mobile app foundation (React Native)

Deliverables:

Working API with user management
Basic React Native shell app
Database schema implemented
External API connections tested

Phase 2: AI & Personalization (3-4 weeks)

✅ Chat interface implementation
✅ Context extraction (mood, energy)
✅ Watch history tracking
✅ Taste profile building
✅ Categorized recommendations
✅ Feedback loop (watched/skipped)

Deliverables:

Functional chat with AI responses
Personalized recommendation engine
Learning loop operational
4 categories with dynamic content

Phase 3: Social & Polish (2-3 weeks)

✅ Friend connection system
✅ Social recommendations
✅ JustWatch API integration (platform info)
✅ UI polish and animations
✅ Beta testing with 20-30 users

Deliverables:

Social features working
Platform availability displayed
Polished UI/UX
Bug fixes from beta feedback

Phase 4: Launch Preparation (1-2 weeks)

✅ Performance optimization
✅ App Store / Play Store submission
✅ Privacy policy & terms of service
✅ Analytics integration (Mixpanel/Amplitude)
✅ Crash reporting (Sentry)

Deliverables:

Apps live in stores
Legal documents published
Monitoring systems active
Launch ready

Explicitly Out of MVP Scope

Features postponed to post-MVP:

❌ Deep platform integration (direct streaming links)
❌ TV series tracking (episode-level recommendations)
❌ Push notifications
❌ Share/export features
❌ Watchlist management
❌ Multi-language support (only TR/EN in MVP)
❌ Web version
❌ Premium tier

Rationale: Focus on core problem first - movie decision fatigue. Add features based on user feedback.

Success Metrics (MVP)

Acquisition:

100 beta users in first month
500 total users by end of month 3

Engagement:

60%+ 7-day retention
3+ recommendation clicks per session
2+ minutes average session time
2+ sessions per week

Feature Adoption:

40%+ users enable social features
70%+ users provide feedback (watched/skipped)
50%+ users return within 24 hours

Quality:

70%+ users watch AI-recommended content (vs skipping)
<5% negative feedback on recommendations
4.0+ app store rating

10. Business Model & Growth

Monetization Strategy

MVP Launch: Free for All

Build user base and gather feedback
No monetization in first 3-6 months
Focus on product-market fit

Post-MVP: Freemium Model

Free Tier:

Unlimited chat and recommendations
Up to 3 friends
Basic categories (4 per session)
15-20 recommendations per day

Premium Tier ($4.99/month):

Unlimited friends
Advanced categories ("Hidden Gems", "Cult Classics", "Award Winners")
"Deep Analysis" mode (detailed explanations of why recommended)
Watchlist and collections
Priority AI responses (faster)
Ad-free experience
Early access to new features

Alternative Revenue (Low Priority):

Affiliate links for streaming platforms (minimal expected revenue)
Platform sponsorships (future consideration)

Growth Strategy

Organic Growth (Primary):

Viral Loop: Friend recommendations → invites → growth
- "Your friend X joined FlickPick" notifications
- Improved recommendations when friends join
- Incentive: Better AI with more friends
Content Marketing:
- Blog posts: "The Psychology of Netflix Scrolling"
- SEO: "What to watch on Netflix tonight"
- Medium articles shared in film communities
Community Engagement:
- Reddit: r/NetflixBestOf, r/MovieSuggestions
- Ekşi Sözlük: Organic presence in relevant entries
- Twitter/X: Daily film recommendation threads
Social Proof:
- Instagram Reels: "AI recommended this and it was perfect"
- TikTok: Short format content discovery stories
- User-generated content encouraged

Paid Acquisition (Post-MVP with Budget):

Instagram/Facebook ads (target: 25-40, streaming users)
Google Search ads ("what to watch", "movie recommendations")
TikTok ads (younger demographic)
Micro-influencer partnerships (film critics, reviewers)

Retention Strategies:

Daily gentle notifications: "Ready for tonight's pick?" (opt-in)
Weekly digest emails: Personalized recommendations
Milestone celebrations: "50 movies watched! 🎉"
Friend activity: "Ali watched something new" (subtle)

Competitive Analysis

Existing Solutions:

Product	Strength	Weakness	FlickPick Advantage
Letterboxd	Social, reviews, lists	No decision help	We solve "what now?"
JustWatch	Search, platform info	No personalization	Context-aware AI
Reelgood	Cross-platform catalog	Generic recommendations	Mood-based curation
TasteDive	Taste-based suggestions	No context awareness	Real-time mood matching
Netflix UI	Content owner	Generic, overwhelming	Curated daily picks

FlickPick's Unique Position:

Only solution combining: chat-based context + AI personalization + social influence + minimalist UX
Solves decision fatigue, not just discovery
Works across all platforms (not locked to one service)

Risk Factors & Mitigations

Technical Risks:

Risk	Impact	Mitigation
OpenAI API costs scale with users	High	Gradual migration to fine-tuned smaller model by month 6
TMDb API rate limits	Medium	Aggressive caching, strategic queries, fallback to local data
JustWatch API reliability	Medium	Daily sync, user feedback for corrections, fallback messaging
Recommendation quality issues	High	Continuous A/B testing, user feedback loops, manual curation backup

Business Risks:

Risk	Impact	Mitigation
"Another app?" user resistance	High	Super smooth onboarding, immediate value, free tier
Low friend network effects	Medium	AI works great solo, social is bonus not requirement
Streaming platform changes	Low	Platform-agnostic approach, not dependent on deep integrations
Privacy concerns	Medium	Transparent policy, user controls, no data sales

Market Risks:

Risk	Impact	Mitigation
Netflix improves their own recommendations	Medium	We offer cross-platform, they can't
Copycat products	Low	Speed to market, brand building, community
AI costs make business unsustainable	High	Premium tier, efficient models, caching strategies

11. Future Vision (12-24 Months)

Year 1 Roadmap (Post-MVP)

Q1-Q2: Core Expansion

TV series tracking (episode recommendations, progress tracking)
Advanced filtering (runtime, decade, mood tags)
Watchlist and collections
Push notifications (smart timing)
Web version (desktop browser)

Q3-Q4: Content Expansion

Book recommendations (integrate Goodreads)
Podcast suggestions (Spotify/Apple Podcasts)
"Tonight's Plan" mode: Movie + dinner suggestions
Group recommendations: "What should we watch together?"

Year 2 Vision

Entertainment Ecosystem:

Voice-only mode (hands-free, Siri/Google Assistant integration)
Smart TV apps (Apple TV, Android TV, Fire TV)
Theater integration (cinema showtimes, ticket booking)
Event suggestions (concerts, live shows based on taste)

AI Evolution:

Custom avatars (personality, voice customization)
Multi-modal input (voice, image, emoji)
Predictive recommendations (anticipate mood before asking)
Family/household modes (multiple profiles, kid-safe)

Social Features:

Watch parties (sync viewing with friends)
Shared watchlists (couples, families)
Discussion threads (spoiler-safe)
Taste compatibility scores with friends

Ultimate Vision (3-5 Years)

FlickPick as "Instant Entertainment Decision Assistant"

From morning coffee to evening relaxation, FlickPick eliminates all micro-decisions:

Morning: "What to read with coffee?" (book/article)
Lunch break: "Quick podcast?" (15min commute)
Evening: "Movie night?" (with friends, family, or solo)
Weekend: "What to do?" (cinema, museum, concert)

Key Principles:

Zero friction: Open app, get answer in <10 seconds
Context-aware: Knows your schedule, mood, companions
Passive learning: Never feels like work
Privacy-first: Your data, your control
Platform-agnostic: Works with any service

12. Technical Challenges & Solutions

Challenge 1: OpenAI API Cost Management

Problem:

Each chat costs $0.01-0.03
1,000 daily active users = $10-30/day = $300-900/month
At 10,000 users: $3,000-9,000/month just for chat

Solution Timeline:

Months 1-3: Use OpenAI GPT-4 (rapid development, validate product)
Months 4-6: Fine-tune GPT-3.5-turbo on our conversation patterns (50% cost reduction)
Months 7-12: Custom small model (Llama-based) for context classification (90% cost reduction)
Ongoing: Aggressive caching of similar queries, prompt optimization

Cost Projection:

MVP (100 users): ~$50/month → Acceptable
Growth (1,000 users): ~$500/month → Optimize prompts
Scale (10,000 users): ~$500/month with custom model → Sustainable

Challenge 2: Cold Start Problem

Problem: New users have no history → poor recommendations → bad first impression

Solutions:

Optional Quick Taste Test (30 seconds):
- Show 8-10 popular movie posters
- "Tap what you'd watch" (multi-select)
- Extract genre/style preferences instantly
Smart Defaults:
- First recommendations: Popular + critically acclaimed
- Platform-specific trending content
- Use time/day context (Friday evening = lighter content)
Social Bootstrap:
- If user connects friends immediately, use their data
- "Your taste is similar to Alex" → borrow preferences
Rapid Learning:
- First 3 feedback cycles weighted 3x normal
- After 5 interactions, quality dramatically improves

Challenge 3: Recommendation Diversity

Problem: Filter bubbles - users get stuck in same genres

Solutions:

Exploration Factor:
- 20% of recommendations outside comfort zone
- "Expand Your Taste" category (opt-in)
- Serendipity boost for highly-rated outliers
Context Variation:
- Different moods unlock different genres
- Time-based diversity (weekday vs weekend)
- Friend recommendations introduce novelty
Anti-Patterns:
- Don't recommend same genre 3 times in a row
- Avoid actors/directors if user skips them twice
- Rotate categories (not always same 4)

Challenge 4: Platform Data Freshness

Problem: Content moves between platforms constantly (Netflix loses/gains titles)

Solutions:

Daily Sync:
- JustWatch API refresh every 24 hours
- Priority: User's selected platforms
- Cache with TTL (24 hour expiration)
User Feedback:
- "Not available anymore" button
- Crowdsourced corrections
- Auto-update cache on user reports
Fallback Messaging:
- "Last checked: 2 days ago"
- "Availability may vary by region"
- Platform badge with disclaimer

Edge Cases & Solutions

Scenario: User has no streaming platforms selected

Solution: Require at least 1 platform during onboarding
Fallback: Show "Available to rent/buy" content if all platforms unavailable

Scenario: All recommendations skipped/rejected

Solution: AI asks: "What are you really in the mood for?"
Action: Generate completely different category set
Learning: Mark this context as "difficult to predict"

Scenario: No internet connection

Solution: Show last cached recommendations
Indicator: "Offline - showing recent suggestions"
Action: Queue feedback for sync when online

Scenario: No friends connected + social features enabled

Solution: Hide "Friends Loved These" category
Alternative: Show "Other Users Enjoyed" (anonymous aggregate)

Scenario: User writes very long chat message

Solution: AI extracts key context, responds briefly
Acknowledgment: "Got it: Need light comedy after long day"

Conclusion

FlickPick addresses a universal micro-problem—entertainment decision fatigue—through an AI-powered, context-aware, minimalist mobile experience. By focusing on passive learning, social optionality, and rapid value delivery, the app eliminates friction in daily entertainment choices.

Key Success Factors:

✅ Solve real problem (validated by user research)
✅ Minimal user effort (30-second onboarding, 10-second daily use)
✅ Immediate value (curated picks from first session)
✅ Continuous improvement (learns passively from feedback)
✅ Social growth (friend recommendations drive viral loop)

Next Steps:

Validate design with 10-15 target users (mockup testing)
Build MVP (3-4 months development)
Beta launch with 100 users
Iterate based on feedback
Public launch

Document Status: ✅ Design Approved
Ready for: Implementation Planning
Estimated Timeline: 3-4 months to MVP launch
Estimated Budget: $20,000 - $30,000 (development + initial infrastructure)

This document was created through collaborative brainstorming using AI-assisted design methodology.

trkaplan/2025-11-18-flickpick-ai-movie-assistant-design.md

FlickPick: AI-Powered Movie Assistant - Design Document

Executive Summary

1. Problem Statement

Core Problem

Target Users

Why Current Solutions Fail

2. Solution Overview

Product Vision

Value Proposition

Core Features (MVP)

3. User Experience Flow

First-Time Onboarding (30 seconds)

Daily Usage Flow

AI Chat Characteristics

4. Technical Architecture

Technology Stack

System Architecture

5. Data Models

User Model

WatchHistory Model

SocialConnection Model

Content Cache (Redis)

6. AI Recommendation Engine

Recommendation Algorithm Flow

Learning Loop

7. Social Features & Privacy

Social System (Optional)

Privacy & Data Security

8. UI/UX Design

Design Principles

Color Palette

Key Screens

Interactions & Animations

Accessibility Features

9. MVP Development Plan

MVP Scope (3-4 Months)

Explicitly Out of MVP Scope

Success Metrics (MVP)

10. Business Model & Growth

Monetization Strategy

Growth Strategy

Competitive Analysis

Risk Factors & Mitigations

11. Future Vision (12-24 Months)

Year 1 Roadmap (Post-MVP)

Year 2 Vision

Ultimate Vision (3-5 Years)

12. Technical Challenges & Solutions

Challenge 1: OpenAI API Cost Management

Challenge 2: Cold Start Problem

Challenge 3: Recommendation Diversity

Challenge 4: Platform Data Freshness

Edge Cases & Solutions

Conclusion