International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Online communities often struggle with efficient networking due to incomplete information about members’ capabilities, interests, and intentions, making manual networking time-consuming and prone to mismatched partnerships. To overcome this challenge, the project implements a production-ready, AI-driven community networking platform that uses LLM-powered natural language search to intelligently match users based on comprehensive profile analysis, including Ikigai self-discovery responses, professional backgrounds, portfolios, social profiles, skills, and intent. The system features a structured multi-step onboarding process to capture purpose-driven and professional data, AI-assisted matchmaking powered by Google Gemini 2.5 Flash through the Lovable AI Gateway, and a natural language search interface that returns ranked and filterable match lists with percentage-based compatibility scoring, AI- generated match explanations, and highlighted attributes. In addition, the platform includes real-time built-in messaging with conversation threading, customizable privacy controls, and a modern gradient-themed user interface developed using Tailwind CSS and shadcn/ui components. The application is built on a modern, scalable technology stack comprising React 18.3, TypeScript, Vite for build tooling, Supabase for authentication and PostgreSQL database management, real-time subscriptions for messaging, Row Level Security (RLS) policies for secure data access, and a serverless edge-function architecture to efficiently support AI processing.

Downloads: | DOI: 10.17148/IJARCCE.2026.151116