All Articles

Mine Operators Are Now Using Artificial Intelligence

November 16, 2023
x min read

Machine failures at mines are a big issue. And artificial intelligence might be the way to solve it.

What’s happening:

  • Israeli based artificial intelligence company Razor Labs (TA:RZR) has entered into a partnership with a prominent mining company to use their AI technology to prevent machine failure
  • The partnership will integrate one of Razor Lab’s flagship products, known as DataMind AI, into mining operations in Australia, South Africa and the United States

How it works:

  • DataMind AI uses sensors and cameras to monitor machinery and cross reference all the data it gathers to try to accurately predict future failures or maintenance
  • Razor Lab’s then leverages artificial intelligence to build a comprehensive model to better understand when machines are getting close to failure and what early warning signs are typical prior to machine malfunction

Why it matters:

  • Machine failures are an enormous risk for human safety in mining operations and artificial intelligence could potentially unlock new predictive capabilities to combat dangerous mechanical issues

By the numbers:

  • Razor Labs will deploy over 8,400 unique sensors across 14 different mining locations
  • The sensor and camera hardware alone is expected to cost over $9M
  • In total, Razor Lab’s will receive approximately $19M to fully develop the new project

Market reaction:

  • Shares of Razor Lab’s rocketed up over +300% on the announcement of their significant new partnership

The intrigue:

  • Mining giants such as Glencore and Newmont are already using Razor Lab’s artificial intelligence technology for their own operations

Discover what’s next. The world’s biggest ideas, disruptive trends, most exciting early stage companies and groundbreaking entrepreneurs.

By clicking Subscribe you're confirming that you agree with our Privacy Policy.
Thanks for subscribing!
Keep an eye out for a welcome email shortly.
Oops! Something went wrong while submitting the form.