Intelligent Knowledge Orchestration (RAG) Platform
Leading Global Financial Institution
KEY IMPACT
Reduced document retrieval time significantly, increased factual accuracy of generated summaries, and established a governed, reusable architecture now extended across the client's knowledge domains.
The Challenge
A global financial institution faced inefficiencies in information retrieval across policy, compliance, and operations documents. Employees often relied on outdated portals and manual search, resulting in long turnaround times and inconsistent outputs.
Our Solution
Our team engineered a multi-agent Retrieval-Augmented Generation (RAG) system designed to handle unstructured policy and compliance data at scale. The solution used LangGraph-based orchestration to manage the query lifecycle — from classification and contextual retrieval to multi-round reasoning and evaluation. A modular Vector Search adapter was integrated with Unity Catalog, ensuring data lineage and security compliance. The retrieval phase used semantic embeddings optimized for financial terminology, while the synthesis layer dynamically adjusted prompting strategy based on query type (policy lookup, compliance interpretation, advisory summary). Automated evaluation pipelines were established using DeepEval Faithfulness metrics, integrated with MLflow Evaluate, ensuring factual alignment and consistency.
Results & Outcomes
Reduced document retrieval time significantly
Increased factual accuracy of generated summaries, verified via random sampling audits and Offline Eval
Established a governed, reusable architecture now extended across the client's knowledge domains
Technologies Used
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