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Explore how enterprises can transform unstructured documents into compute-ready data assets for AI production systems in this 27-minute conference presentation. Learn why PDFs and raw documents limit AI accuracy, performance, and trust, and discover how Signal65's AI lab addresses these challenges through their proof-of-concept engagement. Understand the critical shift from experimental AI to production-ready systems that require properly formatted data for machines rather than human-readable documents. Examine Signal65's comprehensive approach to data transformation, including semantic decomposition to maintain topic continuity, LLM enrichment for generating metadata such as summaries and keywords, and robust governance through unique IDs and lineage tracking. Discover how this methodology overcomes traditional chunking and vectorization limitations that often lose context and attribution. Analyze real-world performance testing results comparing local GPU processing using L40S and RTX Pro hardware against cloud LLM APIs, revealing significant advantages in latency consistency and predictability. See how the "write once, read many" approach enables reliable access across various AI applications while maintaining full traceability to original documents for governance and security compliance. Gain insights into creating intuitive dashboards that help users understand available data content and suggest relevant queries, solving the common challenge of knowing what questions to ask AI systems.
