This framework extracts and verifies contextual information from various data sources across enterprises, then passes the data to a symbolic multi-layer decision-making engine. The engine leverages insigjhts from both objective data, such as policies and contracts, and subjective data, like historical and usage data.

The compliance verification framework is composed of three components: 1) Knowledge acquisitions, 2) verification Language Model (VER-LLM), and 3) Continuous Improvement. Experimental results indicate that our framework out preforms the traditional LLM approach in accuracy, adaptability, and scalability, offering enterprises a sophisticated tool for compliance enforcement.

​Our ETV framework enhances data quality, handles unstructured data, is efficient and scalable, and improves compliance. As AI continues to transform business operations, ETV will be instrumental in delivering high-quality data necessary for advanced systems to function effectively.