Supercharging Financial Insights with RAG-Based Search on Microsoft SQL Server
This RAG-LLM solution helps financial research teams, portfolio managers, and strategy analysts surface timely, actionable market intelligence by combining semantic search on Microsoft SQL Server with Hugging Face models and GPU-accelerated inference on Red Hat OpenShift on Azure — delivering relevant answers with source citations.
This validated pattern demonstrates how to deploy a Retrieval-Augmented Generation (RAG) architecture using open-source Large Language Models (LLMs) on Red Hat OpenShift in Microsoft Azure, with Microsoft SQL Server (Azure SQL) as the integrated vector store and structured data source.
Combining the scalability of OpenShift, the compliance and manageability of Azure, and the familiarity of Azure SQL, this pattern enables enterprises to build production-ready AI solutions for knowledge discovery, data research and intelligent automation.
- Scalable Market Data Processing
Store public earnings reports, investor presentations, analyst notes, and licensed market research in Azure SQL. Use open-source LLMs on OpenShift to process and summarize vast volumes of unstructured data with semantic search and grounded answers.
- GitOps Automation for AI Workflows
Deploy and manage the entire AI pipeline — from vector indexing to LLM inference — using declarative GitOps workflows with Red Hat OpenShift. Ensure consistent, reproducible deployments across environments.
- Hybrid Retrieval for Better Context
Combine vector similarity search with structured market or economic data stored in Azure SQL to generate contextual, explainable answers for research questions like trend comparisons or historical performance.
- Explainable, Source-Linked Answers
Empower research teams to quickly draft reports, generate competitive comparisons, or create briefing notes, backed by references to original documents for transparency.
- Accelerated Time-to-Insigh
Empower analysts and research teams to surface critical market signals and trends faster by combining real-time vector search with trusted data sources — freeing up time for deeper analysis and higher-value recommendations.
