Building Invesmal: An AI-Powered Startup-Investor Matching Platform with Laravel

As a final-year Software Engineering student, I wanted my Final Year Project to be more than just another CRUD application. That's how Invesmal came to life a Laravel-based platform that connects startups, investors, and mentors using AI-driven matching.
The Problem
Finding the right investor or mentor is hard. Startups struggle to identify investors whose interests align with their industry, while investors sift through hundreds of pitches manually. I wanted to solve this with smart, automated matching instead of a simple directory listing.
What Invesmal Does
Invesmal supports four user roles Student, Investor, Mentor, and Admin and includes 12 AI-driven features built on top of a Laravel backend, including:

• A core matching engine connecting startups with relevant investors
• Skills and personality analysis for founders
• Goal-based matching between mentors and mentees
• Compatibility scoring between startups and investors
• A funding readiness score to evaluate startup preparedness
• A startup health score for ongoing progress tracking

• A recommendation engine surfacing relevant connections
  Each feature is built as an independent service class connected through dedicated controllers and routes, keeping the codebase modular and easy to extend.
  Technical Approach
  The platform is built entirely on Laravel, using:

• Service-oriented architecture for AI features (separating business logic from controllers)

• Blade components for dynamic role-based dashboards

• Livewire for real-time, reactive UI elements without heavy JavaScript

• A structured chat/messaging system for communication between users
  One of the more interesting engineering challenges was migrating a working chat and messaging system from an older version of the project into a redesigned Laravel structure while preserving functionality and fixing layout issues (like a tricky sidebar CSS opacity bug) along the way.
  What I Learned
  Building Invesmal taught me how to:

• Structure a large, multi-role Laravel application without the codebase becoming unmanageable

• Break down "AI features" into practical, testable service classes rather than vague black boxes

• Balance moving fast (using AI coding tools effectively) with writing maintainable code

• Debug real-world Blade/Livewire issues that don't show up in tutorials
  What's Next
  I'm continuing to refine Invesmal's matching algorithms and preparing full testing documentation covering all user roles. I'm also open to feedback from other developers working on similar matchmaking or recommendation-based platforms.

If you're a startup founder, investor, or just curious about Laravel-based AI platforms, feel free to check out more of my work on my portfolio: asfandyarali.vercel.app
I'd love to hear your thoughts or answer any questions about the technical implementation in the comments.