Case Study
AI Matchmaking Platform
AI Matchmaking Platform uses intelligent profile analysis, preference matching, and personalised discovery to help users find relevant connections through a structured digital experience.
- 9 MonthsDelivery Timeline
- 13 SpecialistsProject Team
- 10 IntegrationsConnected Interfaces
Project Overview
AI Matchmaking Platform is a matchmaking solution designed to help users discover compatible connections through profile information, preferences, interests, and personalised recommendations. Users can create detailed profiles, define preferences, explore suggested matches, communicate with relevant connections, and manage their account activity. Internal teams can review user activity, manage profiles, oversee reports, and monitor platform operations through administrative tools. The platform was built to bring profile discovery and matchmaking workflows into one connected experience while using intelligent matching logic to improve relevance. Abhineh Infotech supported product planning, UI UX design, application development, AI integration, testing, and deployment.
Project Idea
The platform was designed to make matchmaking more structured by combining detailed profiles, preference based discovery, intelligent recommendations, communication, and administrative controls.
Traditional matchmaking experiences can make users browse through large numbers of profiles without enough context about relevance. The platform needed a more focused approach where profile information, interests, preferences, and interaction patterns could contribute to more meaningful recommendations and easier discovery.
Abhineh Infotech connected profile creation, preference management, intelligent matching, recommendations, communication, notifications, and administration through a unified architecture. AI services processed relevant profile information and matching criteria, while backend services coordinated recommendations and user activity across the application.
The product principle focused on clarity, speed, and confidence. Users needed to understand why profiles appeared in their discovery experience while moving easily between recommendations, profiles, and conversations. Clear interface patterns helped keep the matchmaking journey simple while supporting more personalised discovery.
Challenges
The platform supported different user roles with different access requirements. Members needed private profiles, discovery tools, preferences, and communication features, while internal teams required broader controls for user management, reports, activity review, and platform administration.
Matchmaking involved varied information across profiles, preferences, interests, compatibility criteria, interactions, recommendations, and account activity. The platform needed structured data handling and consistent matching logic to process this information while presenting relevant results without overwhelming users with unnecessary information.
As the user base and profile volume increased, recommendation processing could create additional backend demands. The platform required efficient APIs, data indexing, caching, and scalable cloud services to support profile discovery, recommendation generation, communication, and account activity during higher usage periods.
Project Milestones We Achieved
| Milestone | Tasks | Timeline | Responsible |
|---|---|---|---|
| Discovery and Planning | Mapped member journeys, profile requirements, matching criteria, recommendation logic, communication needs, administrative workflows, integrations, and architecture. | Month 1 | Product and Engineering Team |
| UI/UX Design | Designed profile creation, preferences, recommendations, discovery, messaging, notifications, account areas, and administrative interfaces. | Month 2 | UI UX Design Team |
| Core Development | Built authentication, profiles, preference services, matching APIs, recommendations, communication, notifications, and administrative foundations. | Month 3 to 5 | Application Development Team |
| Integrations and Testing | Connected AI and communication services, validated matching workflows, tested recommendation APIs, reviewed data handling, and performed load testing. | Month 6 to 8 | AI Integration and QA Team |
| Launch and Optimization | Prepared production infrastructure, monitored recommendation activity, resolved issues, refined matching workflows, and optimised key discovery experiences. | Month 9 | Engineering and Delivery Team |
Project Features
The platform combines detailed profiles, preference management, AI powered recommendations, discovery, communication, and administration to create a structured matchmaking experience.
Profile Creation
Users can build detailed profiles with personal information, interests, preferences, and relevant attributes for matchmaking.
Preference Management
Users can define preferred connection criteria to help personalise profile discovery and recommendation results.
AI Match Recommendations
Intelligent matching processes profile attributes, preferences, and compatibility signals to generate more relevant connection suggestions.
Match Discovery
Users can explore recommended profiles, review relevant information, and interact with potential connections through focused discovery screens.
Communication Tools
Matched users can communicate through structured messaging and receive notifications related to conversations and platform activity.
Admin Dashboard
Internal teams can manage users, profiles, reports, recommendations, activity, and platform information through central administrative controls.
Results
AI Matchmaking Platform launched with connected profile, preference, recommendation, discovery, communication, and administrative capabilities. The delivered product created a structured environment where users could move from profile creation to personalised discovery and interaction.
Centralised administration gave internal teams clearer visibility into profiles, user activity, reports, and recommendation workflows. This supported more consistent platform management while reducing the need to handle different operational activities through disconnected processes.
Users gained a more focused discovery journey supported by preference based recommendations and organised profile information. Instead of relying only on broad browsing, users could review suggested connections through an experience designed around relevant profile attributes and selected preferences.
The platform established a technical foundation for continued AI driven matchmaking development. Its architecture can support additional recommendation signals, improved matching models, richer profile attributes, communication features, analytics, and moderation capabilities as the platform evolves.
Development Process
Explore
- User Journey Mapping
- Matchmaking Research
- Requirement Analysis
- Profile Wireframes
- UI UX Design
- Flow Validation
Implement
- Mobile Development
- Backend Development
- API Development
- AI Services
- Messaging Services
- Recommendation Engine
Execute
- Matching Tests
- Load Testing
- Security Review
- Production Setup
- Model Monitoring
- Journey Optimization
Typography & colors
Plus Jakarta Sans
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abcdefghijklmnopqrstuvwxyz
1234567890
Primary
#0E7490
Secondary
#22D3EE
Tertiary Color
#CFFAFE
Icons Color
#64748B
Text Color
#0F172A
Background Color
#FFFFFF
App Visuals
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Tech Stack
Kotlin supported native Android experiences for profile discovery and communication. Python powered AI matching services and recommendation logic, while FastAPI provided efficient backend APIs. PostgreSQL stored structured profile and preference data, and Elasticsearch supported fast profile discovery and search. Azure provided cloud infrastructure, deployment, monitoring, and scalable service hosting.
- Kotlin
- Python
- FastAPI
- PostgreSQL
- Elasticsearch
- Azure
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