Quick path
Generate the profile + swipe + chat flows with ShipNative, store data in Supabase, add face-match verification + moderation queue, wire RevenueCat for premium, launch into one niche and one city. Safety is day-one work — not v2.
What a shippable dating app actually needs
- Auth: phone or Apple Sign In (privacy matters here).
- Profile: photos, bio, age, intent, interests, location.
- Photo verification: face-match selfie vs profile photo.
- Discovery: filtered list or card swipe deck.
- Matching: both users like each other → match unlocks chat.
- Chat: real-time text, photo, report button.
- Moderation: automated + human review queue.
- Paywall: free limited swipes, premium unlock.
- Safety features: block, unmatch, hide profile, panic button.
The AI prompt to start with
Dating app for [niche: marriage-minded professionals / LGBTQ+ / faith-based / creatives / interest-specific]. ONBOARDING: - Apple or phone sign-in (Clerk). - Capture: name, age, gender/pronouns, intent (relationship/casual/friends), photos (3-6), bio (280 chars), interests. - Selfie verification: match against profile photo. MAIN TABS: 1. Discover: card deck, swipe right = like, swipe left = pass, tap for detail. 2. Matches: list of mutual likes, tap to chat. 3. Chat: real-time messages, photo send, 3-dot menu for Block / Report / Unmatch. 4. Profile: my photos, bio, settings, paywall. FILTERS (premium): age range, distance, intent, interests, verified-only. DATA: Supabase with RLS. Tables: users, profiles, swipes, matches, messages, reports. Supabase Realtime for chat + match notifications. Dark theme, soft accent color.
For prompt structure, see The Best Prompts for Generating iOS Apps with AI.
Matching logic: start simple
Skip ML in v1. Three mechanisms, in order:
- Filters: age range, distance, intent, gender preferences. Query the Postgres table with basic WHERE clauses.
- Mutual like: when A swipes right on B and B swipes right on A, insert a row in
matches. Supabase Realtime pushes to both users. - Simple scoring: rank discover deck by shared interests (count of intersection), recency (active in last 7 days), and verification status.
Add embeddings-based similarity ranking at 50k+ users — earlier, you don’t have the data.
Safety and moderation (non-negotiable)
Apple rejects dating apps that cannot demonstrate safety. Google is similar. Build this before you build features:
- Photo verification: require a selfie that matches the profile photos. Use AWS Rekognition CompareFaces or similar — block account creation until it passes.
- Automated image moderation: run every uploaded photo through AWS Rekognition DetectModerationLabels. Reject explicit, suggestive, or unsafe content before posting.
- Text moderation: run chat messages and bios through OpenAI Moderation. Flag harassment, threats, sexual content (for non-adult apps).
- Report + review flow: every profile and message needs a Report button. Flagged content lands in a Supabase table that a human reviews within 24 hours.
- Block and unmatch: prominent in every chat. Blocking must be bidirectionally permanent.
- In-app safety center: link to tips, emergency numbers by country, how to report.
Real-time chat without a chat stack
Use Supabase Realtime subscribed to a messages table filtered by match ID. Covers typing-indicator and read-receipts via Presence and row updates. Good for up to ~100k MAUs without adding Sendbird or Stream. Beyond that, dedicated chat infrastructure is worth the cost.
Monetization that actually works in dating
- Free tier: limited daily swipes (e.g., 50), matches unlock messaging, basic filters.
- Premium ($12–25/month): unlimited swipes, see who liked you, read receipts, advanced filters, boost visibility.
- One-off boosts: super likes, profile boosts — in-app purchases.
- Wire all of it through RevenueCat — see In-App Purchases for Expo: RevenueCat Setup.
Launch: niche, city, community
Dating apps die without liquidity. The playbook that works:
- Pick a niche with a pre-built community — faith, identity, profession, interest. “For X” beats “for everyone.”
- Launch in one metro first. Liquidity requires geographic density.
- Seed with 200+ invited profiles. Hand-invite via Discord servers, subreddits, or a relevant newsletter. Screenshot-quality profiles, not placeholders.
- Run a waitlist for the first 4 weeks post-launch. Scarcity + curation both boost perceived quality.
- Expand only after 1,000 weekly active users with healthy match ratios (1 match per 20 swipes is healthy).
Common mistakes to avoid
- Trying to be generic. “Better Tinder” never works.
- Launching without photo verification. Apple rejects; users churn to the verified competitor.
- Gender imbalance on launch. Seed ~1:1 and screen for it in invites.
- Complex ML matching before you have users. Simple filters beat ML on small data.
- Ad-supported revenue. Dating is about trust; ads erode trust.
- Launching nationally on day one. City-by-city, always.