Scaling to Millions of Transactions with +90% Spend Classification Accuracy Using Agentic Systems
A Fortune Global 500 manufacturer with $15 billion in annual revenue faced a persistent procurement challenge: millions of annual transactions had to be classified across multiple taxonomies with high accuracy, speed, and compliance.
Inconsistent naming, free-text purchase descriptions, and decentralized procurement structures made reliable classification difficult to scale. Manual processes consumed 12,000 hours annually and delivered a 20% misclassification rate.
PSA, an agentic system built on Oraczen’s Zen Platform, deployed an AI-driven classification capability combining machine learning, Retrieval-Augmented Generation (RAG), large language models (LLMs), and human-in-the-loop (HITL) oversight. The result: 95% classification accuracy, 85% time savings, and $30 million in identified cost-saving opportunities.
