AI and LLM-Based Use Cases for Unstructured Data Processing

Use Case: Regulatory Compliance for Tokenized Asset Transactions

In the case of tokenized assets such as real estate, securities, or commodities, transactions between buyers and sellers must adhere to strict regulatory frameworks. These regulations are often buried within complex legal documents, making it challenging to ensure compliance in an automated, decentralized system. Manually reviewing legal documents for every transaction would be impractical, especially in a fast-paced market.

How IntelliX Leverages LLMs

IntelliX can use large language models (LLMs) to process unstructured regulatory documents, such as local securities laws, anti-money laundering (AML) policies, and know-your-customer (KYC) requirements. When a transaction involving tokenized assets is initiated, the LLMs analyze the profiles of the buyer and seller to ensure compliance with these regulations. The unstructured legal texts are converted into structured data, which is then used to validate whether the transaction can proceed.

For example, an LLM can determine whether a specific buyer is eligible to purchase a security token based on their country of residence and the relevant securities laws extracted from legal texts. If the buyer does not meet the criteria, the transaction is automatically denied, ensuring that all tokenized asset transfers comply with the appropriate regulatory framework.

This use case allows IntelliX to automate compliance checks, reducing the manual effort required to interpret legal documents while ensuring transactions remain compliant with complex regulatory requirements. The ability to integrate unstructured data processing into the transaction flow makes IntelliX a powerful tool for tokenized asset platforms operating in regulated markets.