The Challenge
The client's critical business knowledge was trapped in thousands of unstructured documents (like PDFs, DOCX, and PPTX) scattered across disconnected systems like Microsoft SharePoint and Google Cloud Storage. This made it impossible for employees to find accurate, context-aware answers to complex questions, hindering productivity and decision-making.
Our Solution
We designed and built a fully automated, configuration-driven data pipeline entirely on the Databricks platform. The solution incrementally ingests new or updated files, uses multi-modal LLMs to extract and analyze text and images, and even converts legacy file formats like .doc using LibreOffice for high-fidelity extraction. All processed knowledge is indexed into a Databricks Vector Search index, creating a centralized, queryable knowledge base to power an internal RAG chatbot
Results & Impact
Successfully unlocked previously inaccessible corporate data, enabling the launch of a powerful internal RAG (Retrieval-Augmented Generation) chatbot. This system now allows employees to ask complex, natural language questions and receive accurate, context-aware answers sourced directly from internal knowledge assets, significantly improving productivity, knowledge discovery, and informed decision-making across the business.