MetadataHub Resources
Papers, videos, and more on extracting intelligence once and reusing it everywhere.
White Papers
Redundant Semantic Computation in AI Systems
The hidden cost of reprocessing the same unstructured data again and again, and where the waste actually comes from.
Why Current AI Tools Can't Fix the Token Tax
Vector databases, RAG frameworks, and pipelines all re-derive the same context. Here is why the problem persists.
Eliminating the AI Token Tax (Technical)
The architecture for extracting content and context once and provisioning it to every workflow without re-processing.
AI Token Tax ROI: A 3-Year ROI Model
A practical model for quantifying the savings from extract-once across a multi-year AI infrastructure budget.
Videos
Zuse Institute Berlin: Petabyte-Scale Search
How Zuse Institute Berlin finds 80,000 images at exact resolution in seconds across roughly 200 PB.
Zuse Institute Berlin: Research Workflows
Making scientific archive data AI-ready without losing the context that gives it meaning.
Wasabi + MetadataHub
Intelligent metadata over hot cloud storage: extract once, reuse everywhere.