Real people. Real places. Right now.
On Scene is a location-based dating app built around a simple bet: showing up beats swiping. Code Encoders designed, built, and has kept shipping the product since day one, across the mobile app, backend, and marketing site.
onscenedating.com Check in, get seen, start talking with people who are actually there right now.
Client
On Scene App Inc.
Platforms
iOS & Android
Role
Full-stack build partner, design to production
Engagement
Ongoing since 2023
The Brief
Every dating app promises a match. Almost none prove someone's actually there.
The product started under a different name, Singles On Scene, before repositioning to On Scene for launch. The core idea stayed constant through that whole process: instead of matching people in the abstract, show them who's currently checked in at a bar, coffee shop, gym, or event nearby, right now.
That single idea shaped almost every technical decision that followed, from how location data gets pulled in, to how the app treats a match as something that can happen face-to-face, not just in a chat thread.
The Build
What's actually running under the hood
Code Encoders owns the full lifecycle end to end: Figma product design, engineering, production deployment, and technical documentation, on a straightforward, production-grade stack chosen to ship fast and stay maintainable as the feature list grew.
Design
Figma
Full product and UI design, from wireframes through final screens, before any code is written
Mobile app
Flutter
Single codebase across iOS and Android
Website
WordPress
Marketing site and city-by-city landing pages
Admin dashboard
React
Internal dashboard for managing users, transactions, moderation reports, push notification campaigns, blocked users, and CMS-managed profile taxonomy (ethnicity, religion, political affiliation, and more)
Backend
Node.js / Express
REST API serving the mobile app and admin tooling
Database
MongoDB Atlas
Profiles, check-ins, chat, and subscription state
Infrastructure
AWS
Application hosting plus S3 for media storage
Location data
Google Places API
Venue discovery across 8 selectable categories, pulled from Google's full 96-category place index
Payments
RevenueCat
In-app purchases via Apple Pay and Google Pay
SendGrid
Verification codes and transactional messages
How It Works
The flow, in the app's own words
Five modules, built to feel like one continuous idea rather than five separate features bolted together.
Check in
Browse nearby scenes: bars, night clubs, coffee shops, kava bars, gyms, retail, event venues, restaurants, inside a default radius, either as a list or a color-coded map where pin color maps to who's there. One active check-in at a time; check out to move on. A four-hour auto-checkout keeps the data honest.
Google Places, real-timeSeen on Scene
Once checked in, a lightweight group chat opens for that venue (Feeling, Activity, and Dares), plus the option to view individual profiles and send a wink or a kiss directly, no pre-matching required.
Swipe
A conventional card-swipe layer sits alongside the location-based one, for anyone who'd rather browse from the couch. Winks, kisses, rewinds, and instant chat on a mutual match.
Chat
Matched conversations, plus controlled direct-messaging for premium users. Unmatch, delete, report, and block are all one tap away. Every report routes to a review queue without alerting the other user.
Future check-ins
A premium feature that flips the model around: instead of checking in now, book a venue and time slot in advance and see how many others already have plans there. Booking logic blocks overlapping slots and respects each venue's actual opening hours.
The Snag
Apple didn't buy "dating app" as different enough
The first App Store submission came back rejected under Guideline 4.3(b), Spam: in Apple's read, the app duplicated features already common across a saturated dating category.
Apple App Review
Your app primarily includes dating features that duplicate the content and functionality of similar apps in a saturated category. We encourage you to reconsider your app concept and submit something that offers a unique experience.
Our response
We rewrote the pitch around what actually sets On Scene apart from a standard swipe app: real-time check-ins that show who's physically present at a venue right now, and future check-ins that let users see who's already planning to be somewhere before they arrive. Both lean on shared physical presence and timing, not another matching algorithm. We updated the app's metadata, screenshots, and description to make that distinction explicit and resubmitted.
Resubmitted, approved, and live today on both the App Store and Google Play.
Still On Scene
The build didn't stop at launch
Code Encoders has stayed on as the ongoing technical partner, running weekly change cycles off direct client and field-testing feedback, most turned around in a matter of days.
28 Jul
Display names
Switched profile displays from full legal names to usernames across the app, including thumbnails and the activity feed, for privacy.
28 Jul
Gender vs. orientation split
Separated gender (male / female / non-binary) from sexual orientation as distinct fields, rebuilding filters and matching logic around the cleaner model.
28 Jul
Photo editor
Added in-app cropping, rotation, and zoom to the profile photo upload flow.
28 Jul
Notification campaigns
New admin-side module for sending targeted push campaigns ("you've been near this venue for a while, check in?") via Firebase.
Caught in the Field
A client tester and a friend sat side-by-side at the same venue; one could check in, the other couldn't. The app was reading them as roughly a third of a mile apart. We traced it to GPS drift and tightened the check-in proximity threshold down to roughly a tenth of a mile, close enough to make sitting at the same bar reliably register as being on the same scene.
FAQ's
Frequently Asked Questions
On Scene is a location-based dating app that shows users who is currently checked in nearby, at a bar, gym, coffee shop, or event, rather than matching people in the abstract through swiping alone.
A Flutter mobile app for iOS and Android, a Node.js/Express backend, MongoDB Atlas for data, AWS for hosting and storage, and the Google Places API for venue discovery.
Apple's first review rejected it under Guideline 4.3(b) for duplicating features common across the dating app category. Code Encoders resubmitted with the app's metadata and description rewritten to foreground its real-time and future check-in features, and it was approved.
Over 5 million active users and over 3 million live matches from check-ins, as self-reported by On Scene on its own site.
Yes, the engagement is ongoing since 2023, with Code Encoders running weekly change cycles based on direct client and field-testing feedback.
A growth proposal covering 24 features across four pillars: product foundation, a social graph (stories, follows, discover feed), monetization, and an AI layer called Wingman for venue recommendations and conversation assistance.
Yes. Code Encoders owns the full lifecycle: Figma product design, engineering across the mobile app, WordPress marketing site, and an internal React admin dashboard, production deployment, and technical documentation. The admin dashboard itself covers user management, transactions, moderation reports, push notification campaigns, blocked users, and CMS-managed profile fields.
The Outcome
Where it stands now
5M+
Active users
3M+
Live matches from check-ins
2
App stores, iOS and Android
Figures as published on onscenedating.com, self-reported by On Scene, not independently audited.
What's Next
The next chapter is already scoped
Code Encoders put together a full growth proposal for On Scene's next phase: 24 features across four pillars, anchored by an AI layer branded Wingman.
Pillar 01
Foundation
Fixing the empty-scene cold start and tightening the core loop.
Pillar 02
Social graph
Stories, a follow system, shareable profiles, and a discover feed.
Pillar 03
Monetization
Paid reactions, venue placement, and a creator tier beyond subscriptions.
Pillar 04
Wingman AI
Venue recommendations, timing predictions, and conversation assistance.
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