TenderIQ is an AI-powered Tender Comparison & Analysis System designed to automate the process of evaluating multiple tender documents.
Instead of manually reading hundreds of pages, users can upload multiple tender documents and allow the system to extract text, compare documents, analyze similarities, and generate intelligent recommendations.
The project combines Backend Engineering, Natural Language Processing (NLP), and Machine Learning to simplify tender evaluation for procurement teams and organizations.
Project Status: 🚧 Currently Under Active Development
Organizations often receive multiple tender documents from different vendors.
Manual comparison is:
- Time-consuming
- Error-prone
- Difficult to scale
- Inefficient for large documents
TenderIQ automates this workflow by extracting document content and providing AI-assisted comparison and recommendations.
- User Registration
- Secure Login
- Logout
- Session Management
- Password Encryption using Bcrypt
- Upload Multiple Tender Documents
- PDF Support
- DOCX Support
- TXT Support
- MySQL Database Integration
- Document Storage
- Automatic Text Extraction
- Text Cleaning
- Preprocessing
- Document Parsing
Current Features
- Compare Multiple Documents
- Similarity Analysis
- Comparison Report
- Recommendation Generation
Upcoming
- Semantic Search
- Clause Matching
- LLM-based Analysis
- Intelligent Ranking
User Login
│
▼
Upload Tender Documents
│
▼
Extract Text
│
▼
Preprocess Documents
│
▼
Compare Tender Files
│
▼
Generate Similarity Score
│
▼
AI Recommendation
│
▼
Comparison Report
TenderIQ/
│── app.py
│── config.py
│── requirements.txt
│── README.md
├── backend/
│ ├── models/
│ ├── routes/
│ ├── services/
│ └── utils/
├── database/
├── static/
├── templates/
├── uploads/
├── tests/
- Python
- Flask
- MySQL
- PyMySQL
- Bcrypt
- pdfplumber
- docx2txt
- Scikit-learn
- Sentence Transformers
- Transformers
- PyTorch
- NumPy
- HTML
- CSS (Basic UI - Under Improvement)
- JavaScript (Currently Minimal)
Flask
python-dotenv
PyMySQL
bcrypt
pdfplumber
docx2txt
scikit-learn
sentence-transformers
transformers
torch
numpy
requests
Or install directly
pip install -r requirements.txtgit clone https://github.com/Astik97/TenderIQ.git
cd TenderIQWindows
python -m venv venv
venv\Scripts\activateLinux
python3 -m venv venv
source venv/bin/activatepip install -r requirements.txtCreate
.env
Example
MYSQL_HOST=localhost
MYSQL_USER=root
MYSQL_PASSWORD=your_password
MYSQL_DATABASE=tenderiq
SECRET_KEY=your_secret_key
Import
database/schema.sql
into MySQL.
python app.pyOpen
http://127.0.0.1:5000
- Flask Backend
- User Authentication
- Session Management
- MySQL Integration
- Multi-file Upload
- PDF Processing
- DOCX Processing
- TXT Processing
- Document Storage
- Basic Similarity Analysis
- Comparison Report
- NLP Pipeline
- AI Recommendation Engine
- Clause-Level Comparison
- Semantic Search
- Report Optimization
- Docker Deployment
- AWS Deployment
- REST API
- JWT Authentication
- OCR for Scanned PDFs
- Admin Dashboard
- Multi-user Support
- PDF Report Export
- Enterprise Deployment
- LLM Integration
- Role-Based Access Control
- Password Hashing using Bcrypt
- Session-based Authentication
- Environment Variables
- SQL Injection Protection
- Secure Database Connectivity
Current test modules include
- Text Extraction
- Document Processing
- Similarity Functions
Additional automated testing will be added in future releases.
Contributions, suggestions, and feedback are always welcome.
Feel free to fork the repository and submit a pull request.
Astik Mohapatra
🎓 B.Tech – Computer Science & Engineering
Government College of Engineering, Keonjhar
Target Roles
- Python Developer
- Flask Developer
- Backend Developer
- AI Backend Developer
https://linkedin.com/in/astik-mohapatra
🔗 GitHub
If you found this project useful, please consider giving it a ⭐ on GitHub.
Your support motivates future development of TenderIQ.
This project is currently under active development. New AI capabilities, REST APIs, Docker support, cloud deployment, and advanced NLP features will be added in future releases.