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Kadoa for scraping automation

Put Your Scrapers on Autopilot

Automate your web scrapers and scale instantly. No lengthy setup, no maintenance, no engineering bottlenecks. Do more with less.
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Success story

Problem
A top hedge fund maintained 1000+ web scrapers across teams and pods, with multiple engineers dedicated full-time creating and maintaining them. Their web data strategy was limited by engineering bandwidth, missing critical signals while debugging broken scrapers.
Kadoa Solution
  • Migrated 300+ scrapers to Kadoa in 6 weeks
  • Self-healing technology eliminated most maintenance
  • Reliable data change tracking across scraper runs
  • Direct integration with existing data warehouse
ROI
  • Engineering time saved 120 hours/week redirected to data science work
  • Data coverage expansion 5x more sources with same team size
  • 40% cost savings vs. in-house development
"Kadoa handles all of our scraping so our data scientists can focus on strategic work, not maintaining scrapers."
— Head of Data, Top Hedge Fund
Sample workflow

Any Website

News, filings, prices, social media

APIs & Feeds

REST, GraphQL, WebSocket

Documents

PDFs, reports, presentations

Monitor

Continuous change detection

Extract

Self-adapting data extraction

Validate

Quality checks and anomaly detection

Data Warehouse

Snowflake, BigQuery, S3

Trading Systems

Direct API integration
Sample results
Data Source
SEC Filings
# Scrapers
47
Uptime
99.9%
Maintenance (Before)
20 hrs/week
Maintenance (After)
0 hrs/week
Quality Score
99.8%
Data Source
News Sites
# Scrapers
82
Uptime
99.7%
Maintenance (Before)
35 hrs/week
Maintenance (After)
2 hrs/week
Quality Score
98.5%
Data Source
Company Websites
# Scrapers
156
Uptime
99.9%
Maintenance (Before)
45 hrs/week
Maintenance (After)
1 hr/week
Quality Score
99.2%
Data Source
E-commerce
# Scrapers
38
Uptime
99.8%
Maintenance (Before)
15 hrs/week
Maintenance (After)
0 hrs/week
Quality Score
97.8%
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:
Real-Time MonitoringCompany DocumentsRetail IntelligenceLocation IntelligenceJob Market DataETL for LLMs
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