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💼 Enterprise AICompleted

Advanced Multimodal Agentic RAG

Enterprise RAG platform with multi-database connectivity enabling natural language data interaction.

LangChainLangGraphAzure SQLAWS S3PostgreSQLFastAPIReact

Overview

Advanced Retrieval-Augmented Generation system with agentic capabilities that connects to multiple enterprise data sources including Azure SQL, AWS S3, PostgreSQL, and more. Enables users to have natural conversations with their data across multiple databases and file systems using multimodal understanding.

The Problem

Enterprise data was split across Azure SQL, AWS S3, PostgreSQL, and file systems. Answering one business question meant knowing which system held the data, who could query it, and how, so most questions simply didn't get asked.

The Approach

  1. 01Built an agentic query planner (LangGraph) that decomposes a natural-language question and decides which sources to hit
  2. 02Implemented natural-language-to-SQL/API translation with multimodal document understanding for unstructured sources
  3. 03Added retrieval evaluation and causal analysis so answers are graded for grounding, not just generated
  4. 04Closed the loop with iterative prompt optimization; the system tunes its own retrieval prompts from evaluation results

The Outcome

4+data sources behind a single conversational interface
Gradedretrieval evaluation; answers are scored for grounding
Self-tuningiterative prompt optimization built into the loop

Key Features

  • ✓Multi-database connectivity (Azure SQL, AWS S3, PostgreSQL)
  • ✓Agentic query planning and execution
  • ✓Natural language to SQL/API translation
  • ✓Multimodal document understanding
  • ✓Context-aware response generation
  • ✓Enterprise-grade security and access control

Use Cases

  • Enterprise data exploration and analysis
  • Cross-database business intelligence
  • Document Q&A across file systems
  • Automated report generation