Smarter Hiring Starts with Understanding.
“Because matching talent isn’t just about keywords.”

HireSense.ai is an enterprise-grade AI Talent Intelligence Platform designed to transform traditional hiring workflows into data-driven, context-aware decision systems.

Powered by Generative AI and advanced NLP architectures, HireSense.ai semantically evaluates resumes and job descriptions — moving beyond keyword matching to understand true candidate-job alignment.

At its core, the platform integrates a hybrid RAG (Retrieval-Augmented Generation) + CAG (Context-Aware Generation) pipeline, LLM-based reasoning, FAISS-powered vector retrieval, and cross-encoder re-ranking for explainable, high-precision candidate matching.

By combining semantic embeddings, contextual retrieval, and generative justifications, HireSense.ai delivers human-readable insights, interpretable match scores, and an adaptive framework ready for integration into modern ATS and HR ecosystems.

Objective: To redefine resume intelligence by transforming traditional keyword-based parsing into an LLM-powered, RAG-enhanced semantic pipeline that evaluates candidates through contextual embeddings, intent alignment, and explainable AI-driven scoring — bridging the gap between human understanding and automated recruitment.

🔗 Watch the Presentation on YouTube

Description:
A detailed overview of the conceptual design, architecture, and AI pipeline of HireSense.ai — including semantic embeddings, RAG-based retrieval, and LLM-driven justification generation.

🔗 Watch the Live Demo on YouTube

Description:
An end-to-end demonstration of HireSense.ai in action — showcasing resume and job description ingestion, intelligent candidate scoring, and AI-generated rationale in a responsive web interface.

The home screen presents a refined, dark-themed interface with a focus on simplicity and clarity.
Users can upload resumes or paste text directly alongside the Job Description field.

This stage allows users to either upload a resume file (PDF/DOCX) or paste resume text.
Job descriptions are entered side-by-side, maintaining a structured workflow.

Once analyzed, HireSense.ai presents semantic similarity scores, final AI-adjusted match ratings,
and a human-readable justification explaining the candidate’s fit for the role.

User Uploads Resume & JD
│
▼
Frontend (index.html)
│
▼
Flask Backend (app.py)
│
├── src/data_loader.py → Loads resume/JD, GitHub, LinkedIn data
├── src/candidate_analyzer.py → Embedding, matching, and scoring logic
│
▼
AI Pipeline (RAG + LLM)
│
▼
Final Output: Match Score (0–10) + AI Justification

project-root/
│
├── app.py                          # flask entrypoint
├── requirements.txt           #python dependencies
│
├── src/
│ ├── candidate_analyzer.py                # ai embedding & scoring logic
│ ├── data_loader.py                           # input preprocessing (resume/JD)
│ └── utils/                                          # helper functions and utilities
│
├── templates/
│ ├── index.html                           # frontend UI
│ ├── result.html                           # result visualization
│
└── docker/
├── Dockerfile                           # container build definition
└── run.sh                                 # helper script to build and run container\

1. Clone the Repository

git clone https://github.com/AyushAI/HireSense.ai.git\
cd HireSense.ai

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate        # macOS/Linux
venv\Scripts\activate           # Windows

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

python app.py

1. Build the Image

bash docker/run.sh

Or manually:

docker build -t hiresense-ai -f docker/Dockerfile .

2. Run the Container

docker run -p 5000:5000 hiresense-ai

Resume Example

AI Engineer with 3+ years of experience in NLP, deep learning, and LLM-based architectures.
Built end-to-end GenAI applications using LangChain and HuggingFace.

Job Description Example\

We are seeking a Machine Learning Engineer with strong understanding of RAG pipelines, embedding models, and LLM integration for production-grade AI systems.

• Hybrid RAG + CAG Pipeline for contextual candidate understanding

• LLM-Based Re-Ranker for interpretable justification generation

• Modular AI Architecture designed for extensibility and fine-tuning

• Responsive Frontend UI with smooth transitions and dark/light mode

• Seamless Integration potential with ATS and HR systems

• Dockerized Build for production-grade reproducibility

📚 Future Enhancements

• Integration with Applicant Tracking Systems (ATS)

• Multi-model evaluation (OpenAI, Gemini, Ollama, Mistral)

• Interactive dashboard for HR analytics & insights

• Resume parsing for multilingual datasets

• Continuous fine-tuning via human feedback loop

• Ayush Mahadeo Wase
• Artificial Intelligence Engineer
• [email protected]

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.