Automated Document Intelligence and Evaluation System

The Challenge

A major energy operator needed an efficient way to process and evaluate thousands of daily operational and compliance documents. Manual verification led to inconsistent results and delayed decision-making in field operations.

Our Solution

We designed a Databricks-based Retrieval-Augmented Generation (RAG) workflow to automate document interpretation and validation. The planning phase focused on establishing a LangGraph-powered multi-agent architecture capable of parsing structured and unstructured data across Excel, PDF, and operational reports. A Delta Lake-backed ingestion layer standardized extracted data, while an Agent Evaluation Framework was introduced to ensure every model-generated output met factual accuracy and operational compliance benchmarks. The pipeline leveraged DeepEval to continuously assess agent reliability and MLflow Evaluate for precision tracking and performance benchmarking. A modular orchestration pattern allowed the RAG system to dynamically scale across multiple use cases — from report summarization to anomaly flagging — while maintaining audit traceability and explainability within Databricks.

Results & Outcomes

Technologies Used