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Exhibitors

Austmine 2023

Veracio

Stand: 520
  • | Artificial intelligence and machine learning
  • | Automation software and services
  • | Big Data Analytics
  • | Software - geological
  • | Mineral analysis, sorting and separation systems
  • | Geotechnical equipment, technology and services

Unlocking the value and utility of scarce resources.

Through a fusion of science and technology, Veracio is an orebody technology company that helps miners leverage their opportunities while mitigating the social and environmental impact of their decisions.

With over 100 years of hard-won experience and international pedigree in our field, our team has pioneered ground-breaking technologies to provide clear, accurate data sets that reveal more valuable, actionable information about your orebody than ever before.

 

Address

26 Butler Boulevard
Burbridge Business Park
Adelaide Airport
South Australia
5950
Australia
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Products

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  • The most compact and integrated continuous north seeking gyro tool in drilling. 
  • An expandable downhole platform that allows multiple sensors to be stacked in order to acquire multiple data sets in a single run.
  • 2022 Mining Innovation of the Year. TruScan™ can reduce cost by greater than $80 per meter through faster decision-making with near-real-time geochemical and lithological data.

Videos

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  • Redefining production geology through high-resolution scanning and photography of chips and core.

  • An expandable downhole platform for orebody knowledge and geology that allows multiple sensors to be stacked in order to acquire multiple data sets in a single run. 

  • Through a fusion of science and technology, Veracio helps miners leverage their opportunities while mitigating the social and environmental impact of their decisions.

Blog

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  • Boart Longyear introduces Veracio

    Boart Longyear | Veracio
    A bigger vision for smarter data at a smaller footprint.
  • Mining companies have achieved some impressive results from using machine learning to improve operational performance. So why do so many machine learning projects fail?

News

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