To build this Rag-powered enterprise knowledge copilot, we integrated:
Azure OpenAI
Generated contextual, source-grounded answers from retrieved enterprise information.
OpenAI Embeddings
Converted document content into vector representations for semantic search.
PostgreSQL with pgvector
Stored document embeddings, metadata, and searchable knowledge indexes.
Python and FastAPI
Powered document ingestion, retrieval workflows, authentication, and enterprise integrations.
Microsoft Azure
Provided secure cloud deployment, monitoring, logging, and scalable infrastructure. For a conceptual or anonymized case study, use:
Large Language Model
Generated contextual answers using retrieved and authorized enterprise information.
Embedding Model
Converted documents into vector representations for semantic retrieval.
Vector Database
Stored embeddings and metadata for fast, scalable knowledge search.
Backend Framework
Managed ingestion, retrieval, authentication, analytics, and system integrations.
Cloud Infrastructure
Supported secure deployment, monitoring, logging, and performance scaling.