Hirenix

# Hirenix

## Overview

Hirenix is a full-stack, AI-driven career acceleration SaaS platform designed to optimize the job search process. By leveraging advanced Natural Language Processing (NLP), semantic search embeddings, and Large Language Models (LLMs), Hirenix provides job seekers with deep, actionable feedback that transcends basic keyword matching. The platform acts as a comprehensive career toolkit, offering intelligent resume analysis, GitHub portfolio auditing, tailored job matching, and realistic mock interviews.

## Core Features

### 🧠 AI Resume Intelligence

Unlike traditional ATS scanners that rely heavily on exact keyword matches, Hirenix uses a sophisticated NLP pipeline to understand the context and impact of a candidate's experience.

* Contextual ATS Scoring: Evaluates the quantifiable impact and relevance of experiences.

* Targeted Skill Gap Analysis: Compares the user’s skills against specific job descriptions to identify missing qualifications.

* Actionable Rewrite Suggestions: Generates tailored advice to improve the clarity, brevity, and strength of bullet points using GPT-4o.

### 🐙 GitHub Portfolio Analyzer

Hirenix integrates with the GitHub REST API to perform automated code reviews, providing a technical assessment of a candidate’s portfolio.

* Deep Code Quality Metrics: Audits repositories for architecture, best practices, and code cleanliness rather than just contribution frequency.

* README & Documentation Review: Ensures individual projects are well-documented and present a compelling narrative to technical recruiters.

### 🎯 Semantic Job Matcher & Tailor

The platform provides intelligent job alignment tools to drastically improve application success rates.

* Semantic Matching Engine: Utilizes embeddings (via sentence-transformers) stored in PostgreSQL using the pgvector extension to map the semantic meaning of a resume against target job requirements.

* Automated Cover Letters: Generates highly personalized cover letters dynamically based on the strongest semantic matches between the user's profile and the role.

### 🎙️ Mock Interview Engine

A dynamic testing environment that prepares candidates for technical and behavioral screenings.

* Context-Aware Question Generation: Uses the candidate's uploaded resume and target job description to dynamically generate highly relevant interview questions.

* Real-Time Performance Feedback: Evaluates user responses instantly, scoring them on depth, clarity, and relevance to the role.

## Technical Architecture

Hirenix is built on a modern, scalable architecture separating a high-performance frontend from a numerically intensive backend.

* Frontend (Client): Built with Next.js 16 (App Router) and React 19, written in TypeScript. The UI is styled with Tailwind CSS v4 and animated using Framer Motion, ensuring a responsive, modern, and highly interactive user experience.

* Backend (API): Powered by FastAPI and Python 3.11. This microservice handles heavy ML workloads, API rate limiting, and integrations with third-party providers. Data validation is strictly enforced using Pydantic v2.

* AI & NLP Pipeline: Integrates OpenAI GPT-4o for generative tasks, alongside lightweight local models (**spaCy**, sentence-transformers) for fast, cost-effective vectorization. PDF parsing is handled via pdfplumber and PyMuPDF.

* Database & Persistence: Built on Supabase (PostgreSQL). It heavily leverages the *pgvector** extension for fast similarity searches and Supabase Storage for secure handling of user resumes.

* Authentication & Billing: Secures user data via Supabase Auth and handles subscription management through Stripe.

* Infrastructure: The frontend is deployed via Vercel for edge performance, while the Python backend is hosted on Render. Continuous integration utilizes Husky, lint-staged, Prettier, and ESLint.