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Kadoa for documents

Find, Extract & Standardize Documents

Find relevant company documents and transform them into normalized, analysis-ready datasets.
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Success story

Problem
A global quant firm wanted to collect historical commodity data from over 400 companies, such as web pages, CSVs, PDFs, charts, tables.
Kadoa Solution
  • Automated ingestion of newly published reports
  • Eliminated manual data cleaning and reconciliation
  • Accurate extraction of key KPIs across a wide variety of reports
  • Normalized time series data across all sources
ROI
  • Data standardization 95% automated vs. manual mapping
  • 85% reduction in data preparation time
  • Time to insight from hours to minutes per filing
"Kadoa extracts consistent, normalized datasets from thousands of documents. What took weeks of manual processing now happens automatically."
— Head of Quantitative Research, Quant Fund
Sample workflow

SEC EDGAR

10-K, 10-Q, 8-K, DEF 14A, S-1

Earnings Transcripts

Call transcripts and presentations

International Filings

Global regulatory documents

Extract

Tables, metrics, and text analysis

Normalize

Units, regions, timeframes

Research Platform

Direct model integration
Sample results
Company
FreePort-McMoRan
Commodity
Copper Concentrate
Operation
Bagdad Mine
Production
47.2
Unit
kt
Period
Q1 2024
Company
Southern Copper
Commodity
Copper (SX-EW)
Operation
La Caridad
Production
23.8
Unit
kt contained
Period
Q1 2024
Company
Antofagasta
Commodity
Copper Concentrate
Operation
Los Pelambres
Production
89.1
Unit
kt payable
Period
Q1 2024
Company
BHP
Commodity
Copper Concentrate
Operation
Escondida (57.5%)
Production
201.3
Unit
kt
Period
Q1 2024
Company
Glencore
Commodity
Zinc Concentrate
Operation
Mount Isa
Production
124.6
Unit
kt contained
Period
Q1 2024
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

Ready to turn unstructured data into insights?

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Other Kadoa use cases:
Web Scraping AutomationReal-Time MonitoringRetail IntelligenceLocation IntelligenceJob Market DataETL for LLMs
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