MetadataHub Resources
Papers, videos, use cases, and partner integrations on extracting intelligence once and reusing it everywhere.
The case for extract-once
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.
Get the paperWhy 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.
Get the paperEliminating the AI Token Tax (Technical)
The architecture for extracting content and context once and provisioning it to every workflow without re-processing.
Get the paperAI 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.
Get the paperSee it in production
Zuse Institute Berlin: Petabyte-Scale Search
How Zuse Institute Berlin finds 80,000 images at exact resolution in seconds across roughly 200 PB.
WatchZuse Institute Berlin: Research Workflows
Making scientific archive data AI-ready without losing the context that gives it meaning.
WatchWasabi + MetadataHub
Intelligent metadata over hot cloud storage: extract once, reuse everywhere.
WatchMetadataHub for Life Sciences
How research teams turn petabyte-scale unstructured data into AI-ready intelligence, without losing the scientific context.
WatchWhere MetadataHub pays off
The AI Token Tax
Your AI reprocesses the same files again and again, wasting 40 to 70 percent of your compute budget. See how much you are overspending, and how to stop.
Calculate your Token TaxUse CaseAI-Ready Research Data
AI can see the pixels but not the science. Make petabyte-scale microscopy, imaging, and archive data truly AI-ready, without losing the context that makes it mean something.
See the research approachUse CaseIntelligent Data Archiving
Keep cold data fully searchable. Tier petabytes to low-cost archive while every file stays discoverable and AI-ready, with no rehydration to find what you need.
See the archive approachWorks with the solutions you already use
Panzura Symphony Knowledge Edition
MetadataHub embedded directly in Panzura Symphony. Harvest embedded metadata across the estate, then orchestrate placement, archive, and AI-ready provisioning from one integrated solution.
See the integrationIntegrationSwissVault + MetadataHub
Turn your VaultFS archive into AI-ready data, in-jurisdiction, with no migration and no workflow changes.
See the integrationIntegrationWasabi + MetadataHub
Intelligent metadata over hot cloud storage. Extract once, store cheaply, and keep everything searchable and reusable.
See the integrationIntegrationArcitecta Mediaflux
End-to-end data intelligence and archive at scale: MetadataHub insight paired with policy-driven tiering across the data lifecycle.
See the integrationIntegrationAtlas
MetadataHub insight across your Atlas environment, keeping data discoverable and AI-ready. [Edit this description in code with the real Atlas integration details.]
See the integrationIntegrationNVIDIA AI Factory
Feed your AI Factory clean, context-rich data. MetadataHub prepares unstructured data once so GPU pipelines spend cycles on inference, not preprocessing.
See the integration