Modular Document RAG Framework for Enterprise Knowledge Systems
Enterprise R&D Organization
KEY IMPACT
Delivered a production-ready GenAI framework that reduced new client onboarding from weeks to hours, provided enterprise auditability through integrated evaluation layers, and formed the baseline for all subsequent RAG deployments across industries.
The Challenge
Enterprises required a consistent and reusable framework to implement domain-specific RAG systems capable of ingesting and retrieving knowledge across various file formats and departments.
Our Solution
We developed a LangGraph-based modular Document RAG Framework optimized for scalability, governance, and evaluation. The ingestion pipeline unified structured and unstructured formats (PDF, DOCX, PPTX, XLSX) through a canonical metadata schema stored in Unity Catalog Volumes. Each query flow passed through an adaptive prompt strategy selector ensuring the right synthesis style — direct, contextual, or hybrid. Model outputs were benchmarked through DeepEval Faithfulness metrics and compared against golden datasets tracked in MLflow Evaluate.
Results & Outcomes
Delivered a production-ready GenAI framework that reduced new client onboarding from weeks to hours
Provided enterprise auditability through integrated evaluation layers
Formed the baseline for all subsequent RAG deployments across industries
Technologies Used
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