Building HireHut.tech: An AI-Powered Job Search Platform

Building HireHut.tech was one of my most ambitious projects. The goal was to create an intelligent, AI-native job search partner that goes beyond simple keyword matching and actually understands the user's career trajectory.

With over 6,700 unique visitors utilizing the platform, performance and AI accuracy were paramount. Here's a look at the technical architecture that powers HireHut.tech.

The AI Engine: Beyond Simple Prompts

The core of HireHut is its AI engine. Rather than just wrapping an API call to OpenAI, the platform utilizes a multi-agent orchestration system.

• Profile Analyzer Agent: Parses resumes and extracts hard and soft skills.
• Market Matcher Agent: Compares user profiles against real-time job market data.
• Feedback Agent: Provides actionable advice on how users can improve their resumes for specific roles.

By chaining these agents using LangGraph, the system can provide deep, contextual advice that a single prompt simply cannot achieve.

Scalable Full-Stack Architecture

To ensure the platform remained fast and responsive, I utilized a modern Next.js architecture:

• Frontend: Next.js App Router with React Server Components to keep client-side JavaScript minimal.
• Styling: Tailwind CSS and Shadcn UI for a sleek, professional, and accessible interface.
• Database: PostgreSQL with Prisma ORM, allowing for complex relational queries when matching candidates to roles.

Continuous Learning and Improvement

One of the biggest challenges in building an AI product is handling hallucinations and edge cases. By implementing comprehensive logging and user feedback loops, the HireHut models continuously refine their suggestions, leading to a much higher success rate for candidates.

Building HireHut.tech taught me how to bridge the gap between experimental AI scripts and a robust, production-ready SaaS product.