Multi-Agent AI Analysis
Specialized analyzer, optimizer, and reviewer agents work in sequence to extract issues, improve content, and validate enhancements with detailed explanations.
Project Overview
HireLens is a comprehensive AI SaaS platform designed to transform the traditional resume-building process into an insight-driven, iterative journey. The system is powered by a sophisticated Multi-Agent LLM Pipeline where three specialized agents collaborate to maximize professional impact:Analyzer Agent: Conducts a deep dive into the resume to extract key skills and experience while pinpointing critical gaps, such as weak phrasing or a lack of quantifiable metrics.Optimizer Agent: Leverages advanced AI to rewrite descriptions with high-impact action verbs, ensuring every bullet point highlights measurable achievements.Reviewer Agent: Performs a final quality check by comparing original and optimized content to generate a definitive improvement score (0-100%) and a clear justification for every change.
🎥 Video Explanation
🎯 Project Purpose
Built to solve the problem of ineffective resume optimization. Job applicants lack actionable insights on why resumes fail to progress in hiring processes. HireLens provides AI-driven, context-aware feedback that goes beyond keyword matching to deliver measurable, meaningful resume improvements.
🛍️ Project Checkouts (Key Highlights)
Structured Resume Analysis
Detects weak phrasing, missing metrics, formatting issues, and provides semantic analysis of resume-to-job-requirement alignment.
AI-Powered Content Optimization
Rewrites bullet points with measurable impact, enhances action verbs, and generates improved professional content.
Profile Aggregation
Integrates LinkedIn, GitHub, and portfolio data to consolidate professional information and auto-generate resume content.
Insight-Driven Feedback
Provides percentage-based improvement scores with detailed explanations of why changes enhance hiring effectiveness.
Job-Specific Customization
Analyzes target job descriptions and suggests role-specific optimizations for maximum compatibility.
🧩 Technology Stack
Frontend
Next.js (App Router), TypeScript, Watermelon UI, Vercel deployment
Backend
Node.js API Routes, async processing with Redis/Upstash queues
Cloud & Infrastructure
MongoDB Atlas with flexible schema for resume data
🧠 Challenges & Solutions
Problem: Resume analysis requires understanding both technical accuracy and hiring market expectations.
Solution: Implemented multi-agent pipeline where specialized agents handle analysis, optimization, and validation with context-aware improvements.
Problem: High latency for AI processing could degrade user experience during content optimization.
Solution: Used Redis-based async queue system with multiple LLM providers to ensure fast responses and ensure reliability.
Problem: Fragmented professional data across multiple platforms (LinkedIn, GitHub, portfolio) needed consolidation.
Solution: Built profile aggregation layer that unifies data sources and enables seamless resume generation from multiple profiles.
📂 Project Structure
src/app/api/pipeline/ - Core multi-agent orchestration
src/agents/ - Logic for Analyzer, Optimizer, and Reviewer agents
src/services/ai.provider.ts - Multi-LLM provider integration
src/models/ - Mongoose schemas for Resume and Analysis tracking
src/components/watermelon-ui/ - Custom UI components library
src/lib/db/ - MongoDB connection and configuration
🏆 Hackathon Achievement
🏆 Finalist
OceanLab × CHARUSAT Hacks 2026 — organized by CHARUSAT
👥 Team Members
Dhruvesh ShyaraFull Stack Development & AI Integration
Priyasha YadavBackend & AI Pipeline Optimization
🙏 Special Thanks
CodingGita Team — for continuous support, mentorship, and guidance throughout the hackathon journey.
✨ Key Learnings
- Building production-ready AI-powered SaaS platforms
- Multi-agent LLM pipeline architecture
- Rapid prototyping and deployment under time pressure
⭐ Why This Project Stands Out
- Multi-Agent LLM Architecture: Coordinated AI agents (Analyzer, Optimizer, Reviewer) for sequential resume optimization.
- Insight Generation: Provides measurable impact scores (0-100%) with detailed explanations of improvements.
- Speed & Reliability: Multi-LLM strategy using Gemini for quality and Groq for high-speed inference.
- 48-Hour Build: Entire SaaS platform designed, built, and deployed during CHARUSAT Hacks 2026.
- Scalable Infrastructure: Deployed with Vercel, MongoDB Atlas, and Upstash Redis for production readiness.