Document Semantic Storage Supported by GenAI and Snowflake

The Siemens Energy faced significant challenges with document management and information retrieval. With a vast and continuously growing volume of internal documents, employees struggled to quickly find relevant information. This was especially critical during onboarding, where new team members had difficulty navigating documentation and accessing role-specific knowledge efficiently.

OrbitaLab designed and implemented a GenAI-powered document semantic storage and retrieval solution: Centralized document ingestion from internal repositories and data lake Semantic indexing using embeddings with PGVector and Snowflake RAG-based chatbot leveraging LLMs to answer natural-language queries Backend services built in Python and FastAPI User interface enabling conversational access to documents with direct links to source materials Secure, scalable deployment on AWS with version control via GitHub The solution enables fast, accurate information retrieval while preserving traceability to original documents.