Why enterprise-grade metadata extraction requires AI agents, not just LLMs

Enterprises spend significant resources on extracting data from unstructured documents such as contracts and invoices, often leading to high costs and errors when done manually. Generative AI offers a solution by understanding text as humans do, yet this alone is insufficient for effective data extraction. Box has developed innovations that use agentic AI within a content management platform to enhance data extraction accuracy, by employing techniques like named entity recognition and model-based chunk re-ranking. This approach allows for a more focused extraction process, reducing errors and API costs while maintaining compliance and security. Agentic AI systems are capable of self-correction and iterative reasoning, improving accuracy by up to ten percentage points over traditional methods. By integrating data storage and governance within the same platform, Box ensures that the extracted data remains connected to its source, enabling enterprises to efficiently process diverse document types without compromising security. This method not only simplifies compliance but also allows human reviewers to focus on more complex cases, making it economically and operationally viable for large-scale enterprise applications.

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