LookAlike Match
Overview
LookaLike is a friendship-based social app that helps people discover and connect with others based on genuine compatibility. Users create a profile, browse others on the home screen, and simply like or skip to explore potential connections. Alongside the bio and profile details, every match also shows a match percentage, a unique feature that measures facial compatibility to help users find connections that feel right from the start. LaunchBox Global built the mobile app and a companion admin dashboard to power this experience, delivering the complete platform in 12 weeks within the Social Networking and Friendship space.
Time
12 Weeks
Domain
Social Networking
Our Contribution
End-to-End App & Dashboard Delivery
The Challenge
Building Trust Into Identity Verification
Since matching relies on facial data, users needed confidence that verification was accurate and their data handled securely — designed to feel reassuring, not like a checkpoint.
Keeping Discovery Simple and Engaging
With match percentage, bios, and photos all present, browsing could feel cluttered. We prioritised what matters most so users make quick, confident decisions.
Delivering a Consistent Experience Across Mobile and Admin
Users and admins needed very different tools. We designed both to feel equally polished without one holding back the other.
Preparing the Platform to Scale
We built the architecture with growth in mind from day one, ensuring performance holds steady as match volume and the user base increase.
Bringing Real-Time Features in Without Clutter
Chat, voice messages, and media sharing needed a home without making the app feel heavy. We layered these into a familiar, lightweight interface.
Our Objectives
Discovery & Research
User Research
We spoke with users to understand what makes them stay on a friendship app — and what makes them leave. Trust in the verification step came up as a major factor early on.
Feature Prioritisation
We defined must-haves first: profile creation, browsing, like/skip, match percentage, and chat. Subscription access was built in from the start as a core business requirement.
Competitor Review
We studied other matching and social discovery apps to see what worked, and where facial-based matching could set LookaLike apart from typical swipe apps.
Tech Exploration
We evaluated facial recognition and verification technologies for accuracy and privacy, and selected messaging infrastructure that could scale as the user base grows.
Research Snapshot
Trust in verification — top retention driver
Maya • 24
Primary Persona
I want to meet real people — not bots or fake profiles. Knowing someone's verified makes me actually reach out.
Competitor Edge
Facial-based matching
Where typical swipe apps rely on photos alone, LookaLike's match percentage became the clear differentiator users remembered.
Research Snapshot
Trust in verification — top retention driver
Maya • 24
Primary Persona
I want to meet real people — not bots or fake profiles. Knowing someone's verified makes me actually reach out.
Competitor Edge
Facial-based matching
Where typical swipe apps rely on photos alone, LookaLike's match percentage became the clear differentiator users remembered.
Workflow
Core Functions
We defined the essential building blocks: profile creation, verification, browsing, like/skip, match percentage, and chat.
Wireframes & Prototypes
Mapped flows for verification, discovery, and chat tested early to catch friction before development.
Iterative Sprints
Built features in cycles with real feedback, refining verification accuracy and the discovery flow along the way.
Final Optimisation
Polished performance and visual consistency across the app ahead of launch.
Wireframes
The final product gives users a friendship platform built around trust from the first interaction: verification that feels quick rather than invasive, discovery that stays effortless, and real-time chat, voice messages, and media sharing that make conversations feel native to the app rather than bolted on. Subscription access is enforced cleanly throughout, restricting features automatically on expiry while keeping the experience seamless for active users.
Branding
Logo Construction
The mark pairs a face-scan glyph with a soft rounded container security expressed through warmth, not hard edges.
Colour Palette
Primary
#5E51C9
Trust · Brand
Accent
#408EE8
Connection
Ink
#0A0A1A
Typography
Mist
#EEF0FF
Surfaces
Typography — General Sans
Aa
ABCDEFGHIJKLM · abcdefghijklm · 0123456789
Buttons & Components
Match Percentage · Face Recognition
Facial
Compatibility
The signature metric
Problems & Solutions
Upload selfie
AI scans details
Real match found
Identity Verification Accuracy
Facial verification only works if it holds up against real-world conditions inconsistent lighting, blurry selfies, low-resolution images, and awkward angles — all of which degrade matching accuracy. There was also the risk of spoofing, someone holding up a photo of a photo to bypass verification.
SolutionA Layered Verification Pipeline
We added a client-side pre-validation step that checks brightness, blur, and face positioning before an image is ever submitted, rejecting poor captures immediately. To prevent spoofing, we layered in basic liveness checks (prompted blinks or actions). On the matching side, we calibrated the confidence threshold across varied lighting and demographics — avoiding both false matches and unfair rejections.
Profile Discovery Flow
Users needed a way to browse and act on profiles that felt instant and familiar, without confusion about what liking, skipping, or a match actually meant.
SolutionAn Intuitive Browse & Match Experience
We implemented a clear like/skip interaction with visual cues and smooth transitions, paired with lazy loading so profiles and media load progressively without lag — keeping browsing light even as the profile pool grows.
Interaction History Management
Users needed to track who they liked, who liked them, and who they matched with — without the app feeling cluttered or hard to navigate.
SolutionStructured Activity Tracking
We built dedicated, real-time sections for Likes, Liked Me, and Matches, giving users an organised way to revisit and act on past interactions.
Subscription-Based Access Control
Access to the app is subscription-gated from onboarding onward, and the flow needed to enforce that cleanly — without frustrating users mid-setup or leaving expired accounts in a broken state.
SolutionControlled Access Flow
We built a subscription-first onboarding sequence with validation checks, ensuring only active subscribers complete setup, and automatically restricting all features the moment a subscription lapses.
Tech Stack & Tools
Final Outcomes
The final product gives users a friendship platform built around trust from the first interaction: verification that feels quick rather than invasive, discovery that stays effortless, and real-time chat, voice messages, and media sharing that make conversations feel native to the app rather than bolted on. Subscription access is enforced cleanly throughout, restricting features automatically on expiry while keeping the experience seamless for active users.
Ready to build a trusted, scalable friendship platform?
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