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Kadoa for LLMs

Connect Unstructured Data To LLMs

Extract and transform unstructured data into LLM-ready data.
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Success story

Problem
A leading investment bank needed to build an internal knowledge assistant for their analysts, leveraging their vast repository of research reports, policy documents, and internal wikis.
Kadoa Solution
  • Automated extraction from wikis, websites, and internal PDFs
  • Custom chunking rules for financial documents
  • Automated metadata tagging
  • Direct integration with vector databases
  • Daily synchronization
ROI
  • Reduced implementation time from 6 months to 3 weeks
  • Zero maintenance for their ETL pipelines
  • Thousands of documents processed weekly without maintenance
"Kadoa eliminated our biggest headache in LLM development - making unstructured data from different sources RAG-ready."
— Head of AI
Sample workflow

Documents

PDFs, Word, Excel, Email archives

Internal Systems

Confluence, SharePoint, Notion, Databases

External Content

Websites

Extract

Clean, relevant content

Chunk

Optimal semantic splitting

Embedding

Embed the chunks as vectors

Vector Databases

LLM Context

Sample results
Document Type
Research Report
Source
Internal Wiki
Chunks Created
247
Tokens
125,432
Processing Time
2.3s
Status
Indexed
Document Type
Policy Document
Source
SharePoint
Chunks Created
156
Tokens
78,291
Processing Time
1.8s
Status
Indexed
Document Type
Technical Docs
Source
Confluence
Chunks Created
892
Tokens
456,123
Processing Time
5.1s
Status
Indexed
Document Type
Email Archives
Source
Exchange
Chunks Created
1,234
Tokens
623,456
Processing Time
7.2s
Status
Processing
Document Type
Financial Reports
Source
PDF Repository
Chunks Created
423
Tokens
234,567
Processing Time
3.5s
Status
Indexed
Make Or Buy

Why top firms switched to Kadoa

Team & Budget Impact
In-House:
2 senior data engineers + ongoing maintenance
With Kadoa:
~40% lower operational cost
Setup
In-House:
Rule-based, manual coding, breaks frequently
With Kadoa:
Auto-generated
Data Quality
In-House:
Manual validation, constant quality issues
With Kadoa:
High quality out-of-the-box, automated data validation
Maintenance
In-House:
Was slow, fragile, and costly
With Kadoa:
Fully managed
Time to Dataset
In-House:
2 to 4 weeks per source
With Kadoa:
A few hours
Scalability
In-House:
Linear cost growth, engineering bottleneck
With Kadoa:
10x sources, same cost

Extract the web. Power your decisions.

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Other Kadoa use cases:
Web Scraping AutomationReal-Time MonitoringCompany DocumentsRetail IntelligenceLocation IntelligenceJob Market DataView all use cases
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