Kudra solves the fundamental problem with agentic and RAG systems: direct LLM ingestion destroys document structure and creates noisy analysis.

The Problem:

Most RAG and agentic systems fail on complex documents. Chunks lose context, 
tables break, cross-references disappear, and models waste capacity 
parsing formatting instead of reasoning. The result: poor quality, 
high token costs, and queries that require synthesis simply don't work.

Our Solution:

We use custom trained AI document intelligence to transform documents into 
structured, multi-layer knowledge:

→ Extract accurate information from PDFs and scanned images including tables, text using OCR, checkboxes, images, etc.
→ Entity/relations extraction: extract any entity and relation from any document
→ Enrich: Comprehensive, LLM-generated analysis 
→ Cross-document intelligence: Relationships and patterns that span your corpus
→ Visual grounding: Always available for human-in-the-loop verification

THE RESULT:
- 7-10x reduction in inference tokens
- Superior quality on complex analytical queries
- Cross-document reasoning that actually works
- Consistent performance at any scale
- Table data becomes fully searchable

Who we serve:

Organizations with serious document problems—complex submissions, 
dense reports, technical documents, multi-document analysis.

Kudra

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