ReelSwipe

Swipe films, train smarter recommendations, finish more watchlists.

ReelSwipe is a mobile-first iOS app with a backend that helps movie fans discover, track, and organize films using a Tinder-like swipe experience. Users swipe right for movies they have seen or want to watch, swipe left for movies they do not want, and the app uses TMDB data plus viewing history to personalize future recommendations, lists, and badges.

Business Goals

  • Reach 25,000 registered users within 6 months of launch.
  • Achieve at least 35% 30-day retention among activated users by month 6.
  • Convert 8% of monthly active users to a premium plan for advanced lists and stats within 12 months.
  • Keep CAC below $6 through organic sharing and watchlist-driven referrals.
  • Drive 60% of new signups from referrals, app store discovery, or social sharing by month 12.

User Goals

  • Quickly mark movies as seen, unseen, or not interested in a swipe-based flow.
  • Get personalized movie recommendations based on prior swipes and watch history.
  • Create and manage watchlists for future movies and themed collections.
  • Earn badges and track progress toward movie challenges like completing major franchises.
  • Pick up where they left off across devices without losing lists or history.

Non-Goals

  • Not a full streaming service or source of movie playback.
  • Not a social network with public feeds, comments, or direct messaging in MVP.
  • Not a universal TV-series tracker or book tracker.
  • Not an AI-generated movie review or editorial content platform in MVP.

Cinema Fan Anna, 29 - Anna watches several movies a week and wants a faster way to track what she has already seen. She likes discovering new titles but gets frustrated by repetitive recommendations and scattered notes.

Cinema Fan Anna, 29

  • As a movie fan, I want to swipe through movie posters quickly, so that I can mark my taste with minimal effort.
  • As a movie fan, I want the app to remember what I have seen, so that I do not get the same films recommended repeatedly.
  • As a movie fan, I want to save interesting films into a future watchlist, so that I can decide what to watch later.

Franchise Completer Dmitri, 34 - Dmitri likes setting movie goals such as finishing Marvel or Lord of the Rings. He needs progress tracking and motivation to keep a long watchlist organized.

Franchise Completer Dmitri, 34

  • As a franchise completer, I want badges for completing film collections, so that I feel progress and motivation.
  • As a franchise completer, I want to see my completion percentage for a saga, so that I know how far I am from finishing.
  • As a franchise completer, I want to filter recommendations by franchise or collection, so that I can focus on the next missing title.

Casual Viewer Lena, 23 - Lena watches movies occasionally and often forgets titles she liked. She wants an easy app that helps her remember films and gives simple suggestions without too much setup.

Casual Viewer Lena, 23

  • As a casual viewer, I want to sign up quickly and start swiping right away, so that I get value in under a minute.
  • As a casual viewer, I want the app to infer my preferences from a small number of swipes, so that recommendations feel relevant early.
  • As a casual viewer, I want a simple seen/unseen toggle for each film, so that I can correct mistakes without confusion.

Authentication and Profile · High priority

  • Users must be able to create an account, sign in, and maintain a profile that stores preferences and watch history.
  • Support email sign-up and Apple Sign In for iOS first release.
  • Allow profile setup with preferred genres, languages, and region.
  • Persist swipe history, seen status, lists, and badge progress per user.
  • Support account deletion and export of personal data.
  • Handle guest browsing only if explicitly enabled, with conversion prompts after a limited number of actions.

Movie Discovery Feed · High priority

  • The core product must present a Tinder-like deck of movies that users can swipe left or right to classify titles quickly.
  • Display one movie card at a time with poster, title, year, runtime, and core metadata from TMDB.
  • Swipe right means seen or interested; swipe left means not seen or not interested, with configuration in settings for semantics.
  • Prevent duplicate cards within a session and hide movies already classified unless resurfacing is enabled.
  • Use basic relevance ranking from genres, popularity, recency, language, and prior interactions.
  • Allow manual action buttons for accessibility and for users who do not want to use gestures.

Recommendations and Ranking · High priority

  • The backend should generate personalized movie recommendations from TMDB data and user history.
  • Combine explicit swipes, watchlist saves, and seen history to score candidate movies.
  • Refresh recommendation set daily and on-demand after major profile changes.
  • Exclude movies already marked as seen unless the user requests re-ranking of similar titles.
  • Include explainability labels such as because you liked sci-fi and Nolan films.
  • Fallback to popular/new releases when history is insufficient.

Watchlists and Goals · Medium priority

  • Users need structured lists for future viewing and themed completion tracking.
  • Let users create multiple lists such as Watch Soon, Family Night, or Oscar Winners.
  • Allow drag-and-drop or quick add/remove from movie detail views.
  • Support franchise and collection progress tracking using TMDB collection metadata.
  • Show percentage completion and remaining titles for selected goals.
  • Allow private lists only in MVP, with shareable public lists as a later enhancement.

Badges and Progress · Medium priority

  • The app should gamify completion through badges and milestone tracking to drive repeat usage.
  • Award badges for actions such as first 10 swipes, first list created, or completing a franchise.
  • Track challenge progress automatically from watched titles and franchise membership.
  • Display badge unlock notifications in a lightweight, non-intrusive way.
  • Prevent badge farming by requiring unique titles and verified watch history.
  • Allow future admin-configured badge rules without app updates.

Fast Onboarding to First Swipe

  • Install and open the iOS app.
  • Sign up with Apple or email in under 30 seconds.
  • Choose a few favorite genres, languages, and whether swipes mean seen or want-to-watch.
  • The app loads a first movie deck and shows a short tutorial overlay.
  • User completes first swipe within 60 seconds of opening the app.
  • System begins personalizing the feed immediately after the first 10 swipes.

1. Sign Up and Preferences

  • The user creates an account and sets lightweight preferences before seeing content.
  • Keep onboarding to one screen per concept to reduce drop-off.
  • Validate email format and handle Apple Sign In failures with retry messaging.
  • Offer skip options for preferences, then infer taste from swipes.

2. Swipe Movie Cards

  • The user rates films in a familiar card-stack interface.
  • Right and left swipes update history instantly with optimistic UI.
  • Show clear labels and haptics so users understand action outcomes.
  • If TMDB data is missing poster art, use fallback artwork and metadata.

3. Refine Recommendations

  • The system updates the queue based on interaction history and profile signals.
  • Refresh ranking after each batch of swipes or on pull-to-refresh.
  • Avoid showing already seen titles unless resurfacing mode is active.
  • When data is sparse, prioritize popular, recent, and genre-matched films.

4. Save to Lists and Track Goals

  • Users can save movies and monitor progress toward themed collections.
  • Add from card swipe, detail page, or recommendation screen.
  • Show franchise and collection progress only when TMDB collection data is reliable.
  • Handle duplicate list adds gracefully with a non-blocking confirmation.

5. Earn Badges and Review Activity

  • The app reinforces engagement through milestones and history views.
  • Unlock badge animations after backend validation, not only client events.
  • Provide activity history with filters for seen, saved, and rejected titles.
  • Allow users to undo the last swipe to reduce accidental actions.

Power User and Edge Case Features

  • Franchise completion mode that focuses on missing titles within a collection.
  • Taste reset and re-training controls for users whose preferences changed.
  • Offline queueing for swipes that sync when connectivity returns.
  • Cross-device sync for watchlists, badges, and swipe history.
  • Optional resurfacing mode to re-show skipped titles after 30 or 90 days.
  • Accessibility mode with reduced motion, large controls, and tap-only input.

Clean, Fast, Accessible Mobile UI

  • Poster-first cards with large title text, genre chips, and minimal clutter.
  • One-handed thumb-friendly controls at the bottom of the screen.
  • High-contrast swipe indicators and accessible buttons for every gesture.
  • Fast image loading with skeleton states and cached posters.
  • Respect iOS dark mode, Dynamic Type, and VoiceOver labels throughout.

Anna installs ReelSwipe on her iPhone after getting tired of forgetting films she has already watched. She signs in with Apple, picks a few favorite genres, and starts swiping through a deck of movies in less than a minute.

After a few swipes, the recommendations start matching her taste more closely, and she saves several films into a Watch Soon list. When she completes a Lord of the Rings challenge, she unlocks a badge and can immediately see what is left in other franchise collections.

Over time, ReelSwipe becomes Anna’s lightweight movie memory and discovery layer. She spends less time searching, discovers more relevant titles, and keeps returning because the app turns casual browsing into a personalized habit.

User-Centric Metrics

  • 65% of new users complete their first swipe within 60 seconds.
  • At least 45% of activated users create one or more watchlists within 7 days.
  • 35% of users interact with recommendations at least 3 times per week.
  • Average session length reaches 4 to 7 minutes without increasing abandonment.
  • Badge completion rate for first milestone reaches 25% within 14 days.

Business Metrics

  • 25,000 registered users within 6 months.
  • 30-day retention of 35% among activated users.
  • 8% free-to-paid conversion within 12 months.
  • 20% month-over-month growth in saved movies during the first two quarters.
  • 40% of signups coming from organic referrals, sharing, or app store discovery by month 12.

Technical Metrics

  • 99.9% API uptime monthly.
  • P95 feed response time under 300 ms for cached recommendations and under 800 ms for fresh ranking.
  • Crash-free iOS sessions above 99.5%.
  • Zero critical security incidents and monthly vulnerability scans passing.

Tracking Plan

  • Track signup_completed with method and region.
  • Track onboarding_preferences_saved with selected genres and swipe semantics.
  • Track movie_card_viewed with movie_id, source, and rank position.
  • Track swipe_action with direction, movie_id, and session_id.
  • Track watchlist_added and watchlist_removed with list_id.
  • Track badge_unlocked with badge_type and completion source.
  • Track recommendation_refresh with trigger type and result count.

Technical Needs

  • iOS app built with SwiftUI for fast, native swipe interactions.
  • Backend API built with Node.js and NestJS or Go for recommendation orchestration.
  • PostgreSQL for users, swipes, lists, badges, and progress state.
  • Redis for recommendation caching, rate limiting, and session-level deck state.
  • Background jobs using a queue such as BullMQ, SQS, or Cloud Tasks for sync and recomputation.
  • TMDB API integration with normalized movie and collection data.
  • Analytics pipeline using Amplitude or PostHog for product events.

Integration Points

  • TMDB API for movie metadata, posters, collections, and search.
  • Sign in with Apple for iOS authentication.
  • Email auth via Auth0, Firebase Auth, or custom OTP provider.
  • Push notifications through Apple Push Notification service.
  • Analytics via Amplitude or PostHog.

Data Storage & Privacy

  • Store only necessary personal data: email, auth identifiers, preferences, swipes, and lists.
  • Support account deletion, data export, and deletion propagation to backups within policy windows.
  • Encrypt sensitive data in transit with TLS and at rest with managed encryption keys.
  • Comply with GDPR and CCPA requirements for consent, data access, and retention controls.
  • Avoid storing TMDB poster assets permanently if licensing or caching rules require shorter retention.

Scalability & Performance

  • Cache popular and personalized movie decks to reduce TMDB dependency and latency.
  • Use pagination and lazy loading for long lists, badge histories, and activity feeds.
  • Precompute recommendation candidates asynchronously for active users.
  • Degrade gracefully when TMDB is slow or rate-limited by serving cached metadata.

Potential Challenges

  • TMDB rate limits could slow feed generation; mitigate with aggressive caching, batching, and background sync.
  • Cold-start recommendations may feel generic; mitigate with genre onboarding and popular-title fallback ranking.
  • Swipe semantics may confuse users if right means seen versus want-to-watch; mitigate with a clear onboarding toggle and persistent labels.
  • Badge rules may become complex; mitigate with a server-side rules engine and admin-configurable templates.
  • Duplicate or incorrect TMDB metadata may pollute lists; mitigate with ID-based normalization and data validation checks.

Team & resourcing - Small team - 2 mobile/backend engineers, 1 product designer, part-time PM, and part-time QA.

Phase 1: MVP Discovery Feed · Weeks 1-4

  • iOS app shell with Apple Sign In and email auth
  • TMDB-powered movie deck with swipe left/right actions
  • Basic seen/unseen or want/not interested state handling
  • PostgreSQL user and swipe history schema
  • Initial analytics events and crash reporting

Phase 2: Personalization and Lists · Weeks 5-8

  • Personalized ranking based on swipe history and preferences
  • Watchlists with create, add, remove, and rename actions
  • Movie detail view with TMDB metadata and similar titles
  • Cross-device sync for history and lists
  • Undo last swipe and accessibility controls

Phase 3: Badges and Goals · Weeks 9-12

  • Badge engine for milestones and franchise completion
  • Progress tracking for collections like Marvel and Lord of the Rings
  • Push notifications for badge unlocks and list reminders
  • Recommendation refresh improvements and resurfacing mode
  • Admin tools for badge configuration

Phase 4: Launch Hardening · Weeks 13-16

  • Performance optimization and caching improvements
  • GDPR/CCPA data export and account deletion flows
  • App Store release readiness, QA pass, and observability dashboards
  • Fallback behavior for TMDB outages and rate limiting
  • A/B testing framework for onboarding and swipe semantics

Paste this into Cursor, Bolt, Lovable, or v0 to start building.

Build a mobile-first iOS app called ReelSwipe with a backend API.

Product summary:
A Tinder-like movie discovery app where users sign up, then swipe through movie cards left and right to mark movies as seen/unseen or interested/not interested. The app uses TMDB metadata and the user’s swipe history, watchlists, and collection progress to personalize recommendations. Users can create multiple private lists, track themed goals like completing Marvel or Lord of the Rings, and earn badges for milestones.

Core screens and flows:
1) Authentication: Sign in with Apple and email sign-up/login.
2) Onboarding: choose genres, languages, region, and swipe semantics; keep it skippable.
3) Discovery deck: one movie card at a time with poster, title, year, runtime, genres, swipe left/right buttons, undo, and accessibility-friendly tap actions.
4) Movie detail: full TMDB metadata, synopsis, cast, trailer link placeholder, add to list, mark seen, mark not interested.
5) Lists: create, rename, delete, and manage multiple watchlists.
6) Goals and badges: franchise completion progress, badge gallery, and milestones.
7) Profile/settings: preferences, privacy, account deletion, export data, and swipe semantics toggle.

Data model:
User(id, email, appleId, createdAt, locale, preferredGenres, preferredLanguages, swipeSemantics, onboardingCompleted)
Movie(id, tmdbId, title, year, runtime, genres, posterPath, overview, popularity, releaseDate, collectionId)
Swipe(id, userId, movieId, direction, createdAt, source)
Watchlist(id, userId, name, isPrivate, createdAt)
WatchlistItem(id, watchlistId, movieId, createdAt)
Badge(id, key, name, description, ruleType, threshold)
UserBadge(id, userId, badgeId, unlockedAt)
CollectionProgress(id, userId, collectionId, totalCount, watchedCount, percentComplete)
RecommendationCache(userId, generatedAt, movieIds)

Recommended tech stack:
iOS app in SwiftUI, backend in NestJS or Fastify with TypeScript, PostgreSQL for persistence, Redis for caching and rate limiting, BullMQ for background jobs, and TMDB API integration. Use Sign in with Apple, APNs for notifications, Amplitude or PostHog for analytics, and S3-compatible storage only if needed for cached assets.

Implementation requirements:
Create production-ready app structure, reusable components for swipe cards and lists, API endpoints for auth, recommendations, swipes, lists, badges, and profile settings, plus database migrations and seed data. Make the UX fast, one-handed, and accessible with dark mode, Dynamic Type, and VoiceOver labels. Include error states for empty decks, TMDB outages, offline swipes, duplicate list items, and account deletion confirmations.

Business Idea

Мобильное приложение с бэкэндом для iOS, где человек может зарегистрироваться и как в тиндере листать фильмы влево-вправо, те, которые он видел и те, которые он не видел. И таким образом оно будет с помощью подключения TMDB понимать, какие рекомендовать на основе прошлых просмотров новых фильмов юзеру, и юзер будет иметь возможность создавать списки для будущих фильмов, списки просмотров для будущих фильмов, а также, возможно, получать какие-то бейджики, если он выполняет какие-то задачи, типа посмотреть все фильмы «Властелин колец» или посмотреть все фильмы «Марвел» и так далее. Главное сохранить фичу, в которой человек может влево-вправо листать фильмы, как в тиндере, чтобы определять те, которые он видел и те, которые он не видел.

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    PRD: Мобильное приложение с бэкэндом для iOS