Gaurav Gend

Questionnaire Prepopulation.

• Developed prepopulation question tool using Excel within RAG framework for addressing complex client issues.
• Designed to answer specific user queries based on document content, using a Retrieval-Augmented Generation (RAG) architecture.
• Utilized Azure for cloud computing with advanced AI capabilities.
• Applied prompt engineering strategies to tackle advanced challenges.
• Utilized PostgreSQL for data log storage.
• Handled end-to-end project execution with required services.

Policy Compliance Checker.

• Developed a compliance-checking system to compare standard control files with policy files.
• Implemented a Retrieval-Augmented Generation (RAG) architecture for accurate and efficient comparison.
• Conducted extensive research to design and optimize the architecture for high accuracy and performance.
• Leveraged Azure AI services for natural language understanding and document analysis.
• Built a user-friendly front end using Streamlit for streamlined interaction and visualization.

AI Chatbot for Document Querying.

• Built an AI chatbot capable of processing multiple documents in various formats.
• Designed to answer specific user queries based on document content, using a Retrieval-Augmented Generation (RAG) architecture.
• Utilized Azure AI for natural language processing and document understanding.
• Developed a ChatGPT-inspired user interface for seamless interaction.
• Stored user query history and logs in PostgreSQL for tracking and analytics.

• Scraped Data using various Python libraries such as BeautifulSoup (bs4) and Selenium to extract data from websites
• To Bypass Captcha used Machine learning algorithms and different AI techniques
• Experienced in storing scraped data efficiently using MongoDB, a NoSQL database designed for flexibility and scalability.created API using FastApi
• Mainly focused on YouTube and Google to get data from Videos, comments, and text to biuld model
• Managed and processed data using AWS SQS (Simple Queue Service).Successfully deployed and configured EC2 instances and Lambda functions on Amazon Web Services (AWS) to handle data processing tasks at scale