Mar 2025 – Oct 2025 · 7 mos · Bengaluru · On-site

• Developed an AI-powered system for automated vendor selection and order placement, integrating DotPe API to track stock thresholds in real-time. Vendors were given login access to update prices, and the system selected the most cost-effective option automatically. Purchase orders were auto-generated and sent, eliminating the need for manual vendor communication.
• Built a chatbot using Retrieval-Augmented Generation (RAG) that could answer user queries from CSV files in natural language. Enabled non-technical users to interact with complex datasets easily using a conversational interface powered by LangChain and Streamlit.
• Integrated multiple third-party APIs to fetch and process real-time data across platforms. Automated data pipelines were created for cleaning, syncing, and visualizing data, which reduced manual effort and improved reporting speed.
• Scraped restaurant data including menus and prices from various websites using Python and BeautifulSoup. Organized and cleaned the data for client use, and built detailed dashboards in Looker Studio to present insights in an interactive, visual format.
• Created a virtual AI-based calling assistant using Dialogflow CX and Twilio that mimicked human interaction. The system handled basic queries, bookings, and follow-ups over phone calls, saving backend support time and improving user engagement.
• Designed demand forecasting pipelines using Python’s Prophet and ARIMA models. Automated daily updates with Google Sheets and App Script, and connected results to Looker Studio dashboards for real-time monitoring and decision-making.
• Developed a vendor scoring logic that ranks suppliers based on pricing, delivery speed, and consistency. Integrated the scoring into the automated ordering flow to ensure reliable and cost-effective procurement every time.