AI-Powered Agentic system for CUSO (Credit Union Service Organization)

Overview

The project involved developing multi-agentic system to handle credit union – related data and reports (based on AIRES data, CallReport and others). By leveraging advanced Retrieval-Augmented Generation (RAG), the system analyzes a knowledge base of over 10,000,000 lines of data to provide instant and accurate responses. The solution was designed to solve scalability challenges by support both well-structured and free-form reports and analysis.

Achievements

The system achieved reports generation in the following areas: Mergers and Acquisitions Low-Income Designation Field of Membership CDFI certification Branches Charter conversions and others, drill-down, analysis and full reporting functionality with ability to ask for details for any report data. It completely replaced old “reporting” systems giving even more functionality and user satisfaction. Also we’ve achieved high-fidelity accuracy through automated hallucination detection and control.

Responsibilities

• Designed a sophisticated state-based serial agents and graph architecture to get data, route queries, grade document relevance, and manage multi-step support workflows.
• Created specific data layer able to separate public and “secret” data and do not allow protected data to leak anywhere outside of the system (for protected data we use agents to work with structure only, not with data).
• Implemented a hybrid semantic search layer featuring query expansion and cross-encoder reranking to ensure precise information retrieval.
• Developed an automated "watchdog" infrastructure for incremental indexing, allowing the system to self-update whenever documentation changes.
• Integrated built-in validation mechanisms (to prevent “hallucinations”)
• Containerized the application for seamless cloud deployment and built an internal monitoring dashboard for real-time performance tracking.
• Implemented sophisticated front-end to achieve better user experience

Technologies Used