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Unstructured Data Readiness

Is Your Unstructured Data Ready for AI?
Rabble AI’s Unstructured Data Readiness Tool evaluates your unstructured document collections before embedding, scoring their RAG suitability and identifying the exact issues that will degrade performance.

Enterprise RAG systems underperform not because of model selection,  but because of poor source content quality.

Common Unstructured Data Sources:

  • Documents (PDF, DOCX, TXT)
  • Images (JPEG, PNG) 
  • Emails & messages (EML, MSG)
  • Web content (HTML)
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The Problem?
Most organizations push unstructured data directly into a RAG pipeline without validating it.

Common failure points include:

  • Outdated or contradictory information
  • Duplicate content creating retrieval bias
  • Missing context or incomplete documentation
  • Inconsistent formatting and structure
  • Missing or weak metadata
  • Poor chunking characteristics

These issues lead to:

  • Incorrect or conflicting responses
  • Irrelevant retrieval results
  • Reduced answer confidence
  • Increased hallucination risk
  • Erosion of user trust

The Answer
Rabble AI is an AI-powered Unstructured Data Readiness tool that:

  1. Profiles unstructured document collections
  2. Assesses RAG-suitability across multiple dimensions
  3. Generates an AI data readiness analysis
  4. Provides prioritized action items for remediation

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The AI wave is hitting enterprise data
Every company wants AI agents querying their operational data. But the gap between “data warehouse” and “AI-ready” is massive.

  • BI tools solved “humans querying data”
  • Data Readiness solves “AI querying data”
  • Over 80% of enterprise data is unstructured

Data quality tools exist. But they’re built for humans to read reports and fix things manually.

Data Readiness is built for AI consumption - where the output is clean data and rich metadata, not dashboards.