ReelEats
Turn Food Reels Into Real Restaurant Visits
An AI food discovery app that extracts restaurant and cafe information from reels, images, captions and map links. It converts social media content into structured, searchable spots with ratings, reviews and collections.
You Save Reels. You Forget Restaurants.
Everyone saves food reels and screenshots but nobody can find them when they actually want to eat. Through smart AI development, ReelEats turns that saved content into organized, searchable food spots with real addresses, ratings and directions.
From Saved Reels to Organized Food Spots
Six Ways ReelEats Turns Content Into Real Spots
Reel-to-Spot Extraction
Share any food reel or TikTok directly to ReelEats. AI reads captions, OCR extracts text from frames and the spot gets identified automatically.
OCR Text Recognition
Upload screenshots of restaurant names, menus or map pins. OCR scans visible text and AI filters out noise to find the real spot name.
Google Places Enrichment
Every extracted spot matches against Google Places for verified addresses, ratings, photos, pricing and hours. Partial names work with fuzzy matching.
Smart Collections
Organize spots into personal or shared collections. Add custom covers with emoji and colors. Filter by cuisine type and vibe within each collection.
Map Discovery
See all saved spots on a global map with category filters. Tap any pin to view the full spot details, get directions or add it to a collection.
Social Sharing
Share collections with friends as editable or view-only lists. Collaborate on group food lists for trips, events or neighborhood favorites.
Share. Extract. Visit.
Share content
Share a reel, screenshot or map link to ReelEats. The app accepts any food content format.
AI extracts the spot
OCR reads text from images. AI identifies the restaurant name and details. Google Places fills in the address, ratings and photos.
Save and organize
The spot lands in your collection. Mark it as want-to-visit, rate it after you go and share collections with friends.
Add Spots From Anywhere
Search for spots manually, upload photos for OCR extraction, paste Google Maps links or import entire saved lists. Every method ends with a fully enriched spot entry.
SwiftUI frontend with an AI extraction backend
SwiftUI handles the native iOS interface with MapKit for location features, the same foundation we rely on for iOS app development. Node.js runs the backend API with OCR processing and AI content analysis. Google Places API enriches every extracted spot with real-world data.
Share Collections Your Way
Create personal or group collections with custom emoji covers and color themes. Share with friends as editable or view-only lists for curated recommendations.
What We Solved Building This App
From parsing messy social media content to matching partial names against millions of places.
Social media food content comes in many formats. A reel might show a restaurant name in a caption, a screenshot might have text overlaid on an image, a Google Maps link might be shared in a message. The app needed to handle all these input types and extract the same structured spot data from each one.
We built a multi-input processing pipeline as part of the backend development. Shared reels get their captions and frame text extracted. Screenshots run through OCR. Google Maps links get parsed for place IDs directly. The pipeline normalizes every input type into a common format before AI processing begins.
OCR text from screenshots and reel frames is messy. Overlaid text, decorative fonts, partial visibility and background noise produce unreliable raw extraction. The AI needed to clean this output, identify what looks like a restaurant name versus a random caption and filter out irrelevant text.
A two-stage AI extraction handles the messy OCR output. Stage one cleans and segments the raw text. Stage two analyzes the cleaned text alongside any caption data to identify restaurant names, addresses and contextual clues. The AI uses pattern recognition trained on food content to separate spot info from generic text.
Matching extracted spot names to real-world restaurants required fuzzy matching against the Google Places database. A reel might mention 'that amazing pasta place on 5th' without ever naming the restaurant. The AI had to work with partial information and still find the right match.
We integrated Google Places API with fuzzy matching and location context. When the AI extracts a partial name or description, the system searches Google Places with location hints from the user's area or any geo-tagged content. The best match gets pulled with full details including address, pricing, photos and ratings.
Users needed a way to organize hundreds of saved spots without it feeling like work. Collections, visited status, ratings and search had to feel fast and natural. The food discovery app had to stay simple enough that saving a spot from a reel takes fewer taps than bookmarking the reel itself.
The spot management interface uses quick-action gestures. Swipe to mark visited, tap to rate, long-press to add to a collection. Search works across spot names, locations and collection names. The whole flow from sharing a reel to having a structured spot entry takes under 5 seconds.
Simple Settings for a Simple App
Account settings, notification preferences and privacy controls live in a clean settings panel. The minimal design keeps the focus on discovering food, not navigating menus.
Common Questions About Food Discovery Apps
An AI food discovery app typically costs between $60,000 and $160,000. A basic version with manual spot saving and search starts near $60,000. A full build with AI extraction from reels, OCR processing, Google Places enrichment, collections and social sharing lands between $100,000 and $160,000.
The AI processes multiple signals from the content. OCR reads visible text from images and video frames. Caption analysis identifies restaurant mentions and location references. Pattern recognition separates food spot names from generic social media text. The extracted name then matches against Google Places for verification.
OCR stands for Optical Character Recognition. It reads text from images and video screenshots. In a food discovery app, OCR extracts restaurant names, addresses and menu items visible in food reels and photos. The raw OCR output then passes through AI cleaning to filter noise and identify relevant spot information.
Yes. The AI works with partial names, neighborhood mentions, cuisine types and visual clues. Google Places fuzzy matching combines these partial signals with location context to find the right restaurant. If multiple matches exist the app presents the top candidates for the user to confirm.
After the AI extracts a restaurant name, the app queries Google Places API with the name and location context. Google returns the full business profile including verified address, phone number, website, pricing level, ratings, reviews, photos and operating hours. This data populates the spot entry automatically.
An AI food discovery app takes 3 to 5 months from kickoff. Expect 2 weeks of AI prototyping for OCR and extraction accuracy, 8 to 14 weeks of development across the iOS app, backend API and Google Places integration, 2 weeks of testing with real social media content and 1 week for App Store submission.
A food discovery app should support shared reels (Instagram, TikTok), screenshots, photos with text overlays, Google Maps links, direct URLs and manual text input. Each format needs a different extraction pipeline but all output the same structured spot data for the user.
When a spot has no Google Places match, the app creates a manual entry with whatever information the AI extracted. Users can add the address, photos and details themselves. If the spot appears on Google Maps later, the app can retroactively enrich it with the Places data.
Building a discovery app with AI?
We build iOS apps with AI extraction, OCR processing and location-based features. From food to travel to shopping, the pattern works.