🚀 TenderIQ - AI-Assisted Tender Comparison & Analysis Platform

📖 Overview

TenderIQ is an AI-Assisted Tender Comparison & Analysis Platform designed to automate the process of evaluating multiple tender documents using Natural Language Processing (NLP) and semantic similarity techniques.

The system enables users to upload tender documents in PDF, DOCX, and TXT formats, automatically extracts and preprocesses document content, performs transformer-based semantic clause comparison using Sentence Transformers, identifies similarities and differences, assesses procurement risks, and generates structured comparison reports with executive summaries and actionable recommendations.

Built using Python, Flask, MySQL, Sentence Transformers, Scikit-learn, and HTML, CSS, JS, TenderIQ transforms a traditionally manual and time-consuming tender review process into an intelligent, efficient, and data-driven decision support system for procurement teams.

Project Status: 🚧 Version 1.0

⭐ Key Highlights

AI-assisted Tender Comparison
Transformer-based Semantic Analysis
Clause-Level Matching
Procurement Risk Assessment
Automated Executive Summary
Rule-based Recommendation Engine
Interactive Analytics Dashboard
Secure Flask Backend
MySQL Database Integration
Professional Comparison Reports

🎯 Problem Statement

Organizations and procurement teams often receive multiple tender documents that contain hundreds of technical, commercial, and legal clauses.

Traditional tender evaluation is:

Time-consuming and resource-intensive
Prone to human errors and inconsistent decisions
Difficult to scale for large or multiple documents
Unable to understand semantic similarities between clauses

TenderIQ addresses these challenges by automating document comparison using NLP-powered semantic analysis, enabling faster, more accurate, and data-driven procurement decisions.

✨ Features

🔐 Core Features

Secure User Authentication
User Registration & Login
Password Hashing using Bcrypt
Session Management
Multi-format Document Upload (PDF, DOCX, TXT)
Automatic Document Storage
Document Text Extraction
Text Cleaning & Preprocessing

🤖 AI & NLP Engine

Transformer-based Sentence Embeddings
Semantic Clause Comparison
Cosine Similarity Analysis
Weighted Similarity Calculation
Clause-Level Matching
Confidence Score Generation
Procurement Risk Assessment
Automated Executive Summary
Rule-based Recommendation Engine
Procurement Insights Generation
Interactive Analytics Dashboard

📊 Reporting

Detailed Comparison Reports
Clause-Level Analysis
Similarity Statistics
Procurement Risk Visualization
Dashboard Analytics
Printable Report Generation

📈 Project Statistics

Statistic	Value
Python Modules	20+
Database Tables	3
Supported File Formats	3
NLP Model	all-MiniLM-L6-v2
Risk Levels	5
Dashboard Widgets	4
Comparison Type	Semantic Clause-Level
Authentication	Secure Session-Based
Report Generation	Automated

Screenshots

Home Page

Login Page

Registration Page

Dashboard

Comparison Report

View Tenders

📊 Performance Metrics

Metric	Value
Supported Document Formats	PDF, DOCX, TXT
NLP Model	Sentence Transformers (all-MiniLM-L6-v2)
Similarity Technique	Semantic Cosine Similarity
Comparison Level	Clause-Level
Risk Classification Levels	5
Dashboard Analytics	4 Interactive Widgets
Authentication	Secure Session-based
Database	MySQL
Report Generation	Automated

⚙ System Workflow

Authentication │ ▼ Upload Tender Documents │ ▼ Extract Text │ ▼ Text Preprocessing │ ▼ Clause Segmentation │ ▼ Sentence Embedding Generation │ ▼ Semantic Similarity Analysis │ ▼ Clause-Level Matching │ ▼ Weighted Similarity Calculation │ ▼ Procurement Risk Assessment │ ▼ Automated Executive Summary │ ▼ Analytics Dashboard │ ▼ Comparison Report

🏗 Project Architecture

Frontend (HTML • CSS • JavaScript) │ ▼ Flask Routes │ ▼ Business Services │ ▼ Document Processing Engine │ ▼ NLP Engine │ ▼ Sentence Transformer │ ▼ Semantic Comparison Engine │ ▼ Risk Assessment Engine │ ▼ Recommendation Engine │ ▼ Report Generation Engine │ ▼ MySQL Database

💻 Tech Stack

Backend

Python
Flask

Database

MySQL
PyMySQL

AI / NLP

Sentence Transformers
all-MiniLM-L6-v2
Transformers

Machine Learning

Scikit-learn
Cosine Similarity
NumPy
PyTorch

Document Processing

pdfplumber
docx2txt

Frontend

HTML5
CSS3
JavaScript

Authentication

Flask Sessions
Bcrypt

Developer Tools

Git
GitHub
VS Code

📦 Requirements

Flask
python-dotenv
PyMySQL
bcrypt
pdfplumber
docx2txt
scikit-learn
sentence-transformers
transformers
torch
numpy
requests

Or install directly

pip install -r requirements.txt

🚀 Installation

Clone Repository

git clone https://github.com/Astik97/TenderIQ.git

cd TenderIQ

Create Virtual Environment

Windows

python -m venv venv

venv\Scripts\activate

Linux

python3 -m venv venv

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create

.env

Example

MYSQL_HOST=localhost

MYSQL_USER=root

MYSQL_PASSWORD=your_password

MYSQL_DATABASE=tenderiq

SECRET_KEY=your_secret_key

Import Database

Import

database
      │
      ▼
tender_system_db_users.sql,
tender_system_db_tenders.sql,
tender_system_db_comparison_reports.sql

into MySQL.

Run Project

python app.py

Open

http://127.0.0.1:5000

✅ Completed Features

User Authentication & Authorization
Secure Password Hashing
Multi-format Document Upload
PDF, DOCX & TXT Processing
Automatic Text Extraction
Text Cleaning & Preprocessing
Clause Segmentation
Transformer-based Sentence Embeddings
Semantic Similarity Analysis
Cosine Similarity Calculation
Clause-Level Matching
Weighted Similarity Scoring
Procurement Risk Assessment
Automated Executive Summary
Rule-based Recommendation Engine
Interactive Analytics Dashboard
Comparison Report Generation
MySQL Database Integration

🔮 Future Roadmap

Docker Containerization
AWS Cloud Deployment
OCR Support for Scanned Documents
Multi-language Tender Comparison
Role-Based Access Control (RBAC)
LLM-powered Clause Explanation
Vendor Ranking System
Contract Compliance Checking
Email Notifications
AI Procurement Chat Assistant

🔒 Security

TenderIQ follows secure backend development practices including:

Password Hashing using Bcrypt
Session-based Authentication
Environment Variable Configuration
Parameterized SQL Queries
Secure MySQL Connectivity
Protected User Sessions

🧪 Testing

The application has been manually tested for:

User Authentication
Session Management
Document Upload
PDF/DOCX/TXT Processing
Semantic Similarity Validation
Clause-Level Comparison
Report Generation
MySQL Database Operations

🤝 Contribution

Contributions, suggestions, and feedback are always welcome.

Feel free to fork the repository and submit a pull request.

👨‍💻 Author

Astik Mohapatra

🎓 B.Tech – Computer Science & Engineering

Government College of Engineering, Keonjhar

Target Roles

Python Developer
Flask Developer
Backend Developer

📧 astikm7007@gmail.com

🔗 LinkedIn

https://linkedin.com/in/astik-mohapatra

🔗 GitHub

https://github.com/Astik97

⭐ Support

If you found this project useful, please consider giving it a ⭐ on GitHub.

Your support motivates future development of TenderIQ.

Name		Name	Last commit message	Last commit date
backend		backend
database		database
screenshots		screenshots
static		static
templates		templates
tests		tests
.gitignore		.gitignore
README.md		README.md
app.py		app.py
config.py		config.py
requirements.txt		requirements.txt

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