Your AI Is Reprocessing the Same Data Again and Again
Every workflow re-reads the same files and re-extracts the same content. MetadataHub captures it once and makes all unstructured data instantly reusable — across RAG, analytics, and automation.
Your AI pipelines are reprocessing the same files over and over.
Every new workflow — RAG, analytics, agents, compliance — repeats the entire extraction process: re-tokenize → re-parse → re-chunk → re-embed → pay again.
In data-heavy environments, the same files get reprocessed 2–12 times per year. That silent redundancy wastes 40–70% of your entire AI compute budget.
Calculate Your AI Token Tax
Enter the number of unstructured files in your environment.
Harvest Once. Reuse Forever.
MetadataHub creates a persistent knowledge fabric — extracted insights directly from your storage that every AI workflow can reuse instantly.
Harvest
Extract content from files where they live—object storage, file systems, archives. Hundreds of formats supported.
Persist
Build a searchable knowledge fabric. All extracted insights persist independently of any workflow.
Provision
Feed AI, RAG, analytics, and governance tools without touching original files.
Built for data-intensive AI — at scale.
MetadataHub is purpose-built for organizations running multiple AI workflows on large, fixed-content data collections.
If your pipelines repeatedly reprocess the same terabytes or petabytes—RAG, agents, analytics, training, compliance—you're paying the AI Token Tax.
MetadataHub ends it.
The benefits that matter most.
40–70% Reduction in Redundant Computation
Tokenization, parsing, chunking, and embedding happen once—not every time a new workflow touches the same file.
Never Touch the Original File Again
Whether your data lives on NAS, S3, cloud, tape, or any mix—MetadataHub harvests once and serves all downstream AI workflows.
One Shared Intelligence Layer
No more siloed vector databases or duplicated preprocessing. Every RAG instance, agent, and analyst queries the same authoritative source.
Turn Your Archive into AI Knowledge Base
Petabyte-scale archives—once dark, siloed data—become fully searchable and usable by AI without migration.
Fastest ROI in AI Infrastructure
40–70% lower preprocessing spend + six- to seven-figure annual savings. Payback typically under 6 months.
Measured Impact
Results from production deployments.
Real results from real deployments.
Eliminating the AI Token Tax on 200 Petabytes at Zuse Institute Berlin
How one of Europe's largest scientific data centers stopped reprocessing the same files and made 200 petabytes instantly accessible to AI workflows.
"At Zuse Institute Berlin, managing 200 petabytes meant our scientists repeatedly reopened and reprocessed the same files. MetadataHub changed that — we could finally access the information instantly without touching the underlying storage."
From Petabyte Blindness to Instant AI-Ready Microscopy
MetadataHub transformed years of microscopy research data into instantly searchable, AI-ready assets — without moving a single file.
"In seconds I can find 80,000 images with exactly 300 nm resolution — something I could never do before MetadataHub."
Go Deeper
The theory, economics, and architecture behind eliminating the AI Token Tax.
Redundant Semantic Computation in AI Systems
The first-principles analysis of the AI Token Tax. Why AI pipelines repeatedly parse, tokenize, and embed the same files.
Read PaperWhy Current Solutions Don't Fix the AI Token Tax
Why today's tools cannot solve redundant preprocessing. Vector databases, data lakes, catalogs, caching — none eliminate redundant file processing.
Read PaperHow MetadataHub Eliminates the AI Token Tax
The architectural solution. How a persistent metadata fabric stops redundant preprocessing and integrates with existing systems.
Get PaperAI Token Tax ROI: 3-Year Economics
The financial impact of eliminating redundant preprocessing. Modeled deployments show 140–620% annual ROI.
Get PaperCalculate your AI Token Tax.
Schedule a POC and we'll measure exactly how much you're overspending on redundant AI preprocessing.