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Kadoa for job market data

Automated Job Market Intelligence

Extract job market insights, hiring patterns, salary data, and talent trends at scale
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Success story

Problem
A fundamental equity fund needed to track workforce changes across their portfolio companies to predict performance shifts. Manual monitoring of LinkedIn and job boards was missing of critical signals.
Kadoa Solution
  • Automated monitoring of hundreds of company hiring pages
  • Detection of hiring velocity changes and skill mix shifts
  • Hiring pattern analysis tracking key changes
ROI
  • 95% accuracy in trend prediction
  • Real-time insights vs monthly reports
"When we saw Meta hiring 100+ AI engineers in 3 months through Kadoa's monitoring, we increased our position before their AI announcement. The workforce data gave us an edge."
— Portfolio Manager, Technology Focused Hedge Fund
Sample workflow

Company Career Sites

Websites monitored

Job Boards

Data Ingested

Discover

New postings and removals

Extract

Roles, skills, seniority, location

Classify

Relevancy

Models

Factor generation

Change Alerts

Material workforce changes
Sample results
Company
NVDA
Workforce Signal
AI Engineer Hiring Surge
Change %
+340%
Department
Research & Development
Stock Impact
+8.2% (30d)
Signal Confidence
96%
Company
META
Workforce Signal
Sales Team Reduction
Change %
-25%
Department
Enterprise Sales
Stock Impact
-3.1% (15d)
Signal Confidence
92%
Company
TSLA
Workforce Signal
Manufacturing Expansion
Change %
+180%
Department
Operations - Berlin
Stock Impact
+5.7% (45d)
Signal Confidence
89%
Company
AMZN
Workforce Signal
Senior Talent Exodus
Change %
-15%
Department
AWS Leadership
Stock Impact
-2.4% (20d)
Signal Confidence
94%
Company
MSFT
Workforce Signal
Cybersecurity Hiring
Change %
+220%
Department
Security Products
Stock Impact
+4.1% (60d)
Signal Confidence
91%
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?

Talk to us
Other Kadoa use cases:
Web Scraping AutomationReal-Time MonitoringCompany DocumentsRetail IntelligenceLocation IntelligenceETL for LLMs
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