HireFlow AI is a next-generation AI-powered HR automation platform designed to streamline and automate the recruitment lifecycle using Multi-Agent AI Systems, RAG pipelines, semantic search, and intelligent candidate matching.
The platform helps HR teams:
- Analyze resumes automatically
- Summarize job descriptions
- Match candidates intelligently
- Score applicants using AI
- Automate interview scheduling
- Improve hiring efficiency and decision-making
- Extracts skills, education, experience, certifications, and projects
- Supports semantic resume understanding
- Converts lengthy JDs into structured hiring requirements
- Identifies mandatory and preferred skills
- Uses embeddings + vector search
- Matches candidates based on semantic similarity
- Generates match percentages
- Ranks candidates automatically
Dedicated AI agents for:
- Resume Parsing
- JD Analysis
- Matching
- Scoring
- Scheduling
- Critic/Reviser validation
- Auto scheduling
- Notifications & reminders
- HR workflow automation
Stores:
- candidate history
- HR feedback
- AI outputs
- interaction logs
Tracks:
- AI accuracy
- candidate pipeline metrics
- latency
- hiring analytics
Frontend (React.js / Next.js)
↓
API Gateway (FastAPI)
↓
Multi-Agent AI Framework
├── JD Summarizer Agent
├── Resume Parser Agent
├── Matching Agent
├── Scoring Agent
├── Scheduler Agent
└── Critic/Reviser Agent
↓
RAG Pipeline + Vector Database
↓
Embedding Models + LLM APIs
↓
Database + Memory Layer
↓
Monitoring & Evaluation
| Component | Purpose |
|---|---|
| RAG | Improves factual accuracy |
| Vector Database | Semantic candidate search |
| Embedding Models | Resume/JD vector generation |
| Multi-Agent Framework | AI task orchestration |
| Re-ranking | Better candidate prioritization |
| Memory Layer | Long-term AI memory |
| Prompt Engineering | Optimized AI outputs |
| Monitoring Layer | Performance tracking |
| API Gateway | Secure routing & scalability |
| Redis Cache | Faster response time |
- React.js
- Next.js
- Tailwind CSS
- Python
- FastAPI / Flask
- OpenAI API
- Gemini API
- LangChain
- CrewAI / LangGraph
- SQLite
- PostgreSQL
- FAISS
- ChromaDB
- Pinecone
- Redis
- Prometheus
- Grafana
- Docker
- Docker Compose
HR uploads a job description.
AI extracts:
- skills
- requirements
- responsibilities
Candidates upload resumes.
AI extracts:
- skills
- experience
- education
- certifications
Embeddings compare resumes with job requirements.
Candidates receive AI-generated match scores.
Top-ranked candidates are shortlisted automatically.
Interview slots and notifications are generated automatically.
| Agent | Responsibility |
|---|---|
| JD Agent | Summarizes job descriptions |
| Resume Agent | Parses resumes |
| Matching Agent | Performs semantic matching |
| Scoring Agent | Calculates match scores |
| Scheduler Agent | Handles interviews |
| Critic Agent | Validates AI outputs |
Query
↓
Retrieve Relevant Data
↓
Vector Search
↓
Re-ranking
↓
Context Augmentation
↓
LLM Response Generation
- Redis caching
- Parallel AI agents
- Async processing
- Optimized prompts
- Indexed databases
- RAG pipeline
- Re-ranking
- Multi-agent validation
- Human feedback loop
- Better embeddings
- JWT Authentication
- API Gateway Security
- Rate Limiting
- Role-Based Access Control
- Secure File Uploads
Tracks:
- API latency
- AI response accuracy
- candidate matching precision
- system logs
- model performance
Tools:
- Prometheus
- Grafana
- ELK Stack
docker compose up --buildcd backend
pip install -r requirements.txt
uvicorn app:app --reloadcd frontend
npm install
npm run dev- AI Interview Bot
- Voice-based HR Assistant
- Payroll Integration
- Employee Analytics
- Multi-language Resume Analysis
- Real-time HR Chatbot
- HR Automation
- Resume Screening
- Campus Recruitment
- Enterprise Hiring
- Talent Acquisition Platforms
- Recruitment Agencies
Likhil
AI Developer | Full Stack Developer | Multi-Agent AI Enthusiast
This project is builded in Lyzer Architect.