We developed an end-to-end Retrieval-Augmented Generation (RAG) pipeline using Azure AI services and custom Python-based data indexing, ensuring seamless document processing, search, and AI-driven responses.
Extracted text, key fields, and tabular data from scanned PDFs and complex regulatory documents.
Converted unstructured documents into structured JSON/text for further processing.
Indexed extracted content using Azure AI Search to enable semantic search.
Implemented vector embeddings to improve the accuracy of retrieved document snippets.
Allowed natural language queries to find the most relevant document passages.
Used GPT-based AI models to generate contextual, human-like responses based on retrieved document chunks.
Ensured responses were factually grounded by using only relevant document snippets in the generation process.
Stored raw and pre-processed documents securely.
Provided a centralized repository for document indexing and retrieval.
Ensured scalability and reliability for handling large datasets.