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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Due Diligence Automation for Digital Assets</title>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/4.5.2/css/bootstrap.min.css">
<style>
body {
padding-top: 50px;
background-color: #f8f9fa;
}
.container {
max-width: 900px;
background-color: #fff;
padding: 30px;
border-radius: 12px;
box-shadow: 0 4px 10px rgba(0,0,0,0.1);
}
h1, h2, h3 {
color: #007bff;
}
ul {
margin-bottom: 20px;
}
.thumbnail {
display: block;
max-width: 300px;
margin: 20px auto;
}
</style>
</head>
<body>
<div class="container">
<h1 class="text-center">📊 Due Diligence Automation for Digital Assets</h1>
<p class="lead text-center"><strong>AI-Powered Crypto Fund Due Diligence</strong></p>
<div class="text-center">
<a href="https://drive.google.com/file/d/1D5GI3hBaL3rQXcfnjsXt4R3KMuIhq8pZ/view" target="_blank">
<img class="thumbnail" src="https://img.icons8.com/clouds/2x/video-playlist.png" alt="Technical Demo">
</a>
<p><a href="https://drive.google.com/file/d/1D5GI3hBaL3rQXcfnjsXt4R3KMuIhq8pZ/view" target="_blank">🎥 Watch the Technical Demo</a></p>
</div>
<h3>🚀 Project Overview</h3>
<p>This intelligent platform automates and enhances the due diligence process for digital assets and crypto investment funds. Developed by 4DS2 students at <a href="https://esprit.tn" target="_blank">ESPRIT University</a>, it uses GPT-based models, data pipelines, and AI for trustworthy and scalable assessments.</p>
<blockquote class="blockquote">
<p class="mb-0">From entity extraction and risk profiling to automated reports — Due_Diligence_4DS2 provides an end-to-end AI-driven solution.</p>
</blockquote>
<h3>🔧 Core Features</h3>
<ul>
<li>📡 Automated data ingestion via APIs, PDFs, GitHub, Reddit, etc.</li>
<li>🧠 Custom NLP with risk profiling using spaCy, regex, and blacklist filtering.</li>
<li>🔎 Hybrid semantic + keyword search with Qdrant & TF-IDF.</li>
<li>🤖 AI question generation using Retrieval-Augmented Generation (RAG).</li>
<li>📊 Automated PowerPoint report generation using <code>python-pptx</code>.</li>
</ul>
<h3>🛠️ Tech Stack</h3>
<ul>
<li><strong>Languages:</strong> Python, JavaScript (React)</li>
<li><strong>ML & NLP:</strong> LLaMA 3.2, spaCy, MPNet, Cross-Encoder</li>
<li><strong>Data Pipelines:</strong> Apache Airflow, LangChain</li>
<li><strong>Retrieval:</strong> Qdrant, TF-IDF, Knowledge Graph</li>
<li><strong>Reporting:</strong> python-pptx, Seaborn</li>
</ul>
<h3>📈 Evaluation Metrics</h3>
<p>Evaluation uses <a href="https://github.com/explodinggradients/ragas" target="_blank">RAGAS</a> to measure:</p>
<ul>
<li>Context precision, recall, and relevance</li>
<li>Answer factuality and completeness</li>
<li>BLEU, ROUGE, BERTScore, Exact Match</li>
</ul>
<h3>🌐 Deployment & Access</h3>
<ul>
<li>🚀 FastAPI backend</li>
<li>🔍 Local LLaMA inference</li>
<li>📄 SQLite for document management</li>
</ul>
<h3>📚 References</h3>
<ul>
<li><a href="https://openrouter.ai/meta-llama/llama-4-maverick" target="_blank">Meta LLaMA 3.2</a></li>
<li><a href="https://qdrant.tech" target="_blank">Qdrant</a></li>
<li><a href="https://docs.langchain.com" target="_blank">LangChain</a></li>
<li><a href="https://spacy.io" target="_blank">spaCy</a></li>
</ul>
<h3>🙏 Acknowledgments</h3>
<p>This project was developed under the guidance of the <strong>Faculty of ESPRIT University</strong> by students of 4DS2.</p>
<h3>💬 Contact</h3>
<p>📧 <a href="mailto:info@esprit.tn">info@esprit.tn</a></p>
<p>🌐 <a href="https://bennourines.github.io/Due_Diligence_4DS2" target="_blank">Project Website</a></p>
</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/4.5.2/js/bootstrap.bundle.min.js"></script>
</body>
</html>