Overview:

For enterprise leaders, the most overlooked risk in GenAI isn’t the model—it’s the data. The accuracy, cost efficiency, and scalability of your AI strategy all trace back to how effectively your organization ingests and prepares data. The wrong data platform can quietly undermine your entire initiative, producing hallucinations, inflating compute expenses, and stalling production timelines.

Many teams still assess data tooling based on marketing claims rather than empirical evidence, introducing unnecessary risk into otherwise robust architectures. This session introduces Unstructured’s SCORE Benchmark, a standardized framework that helps enterprise AI and data leaders objectively measure data ingestion and preprocessing quality. You’ll learn how to cut through vendor noise and validate infrastructure decisions with quantifiable metrics, ensuring your GenAI stack delivers trustworthy, production-grade outcomes from complex, unstructured enterprise content.

Details:

In this session, you’ll learn how to:

• Operationalize data quality: Use the SCORE framework as a standardized rubric to evaluate document ingestion and preprocessing quality, and how well different tools fit your architecture.
• Reduce hallucination and compliance risks: See how ingestion accuracy and structural fidelity drive GenAI reliability, auditability, and safe use of enterprise documents.
• Optimize TCO and scalability: Compare how competing ingestion approaches impact compute efficiency, throughput, and cost as you scale from thousands to millions of pages.

This session includes a live Q&A. Can’t make it live? Register anyway and we’ll send you the recording.