Metals & Mining
VERIFIED for short term payback

AI-based contaminant detection solution to prevent equipment damage

Key facts

100%observed reduction in repairs required
over62%observed increase in removed contaminants
$1mpotential maintenance cost savings
Next Steps
Book a call
Email sales

Top business benefits

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Improve throughput

Maximise ore production by avoiding unplanned downtime

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Prevent unplanned downtime

Identify contaminants before they cause damage

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Save maintenance costs

Reduce repairs needed by preventing equipment damage

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Real-time insights

Monitor ore production anytime using a web browser

AI-based solution for mining companies that improves throughput by detecting contaminants on the conveyor before they cause equipment damage. 

Main features

  • Powerful, easy-to-use dashboards

  • Access real-time performance data and insights

  • End-to-end support from Axora and partners

  • AI models customised to your conveyor setup

  • Flexible deployment options (on-prem, cloud)

  • Detects metallic and non-metallic (wood, plastic) contaminants

Deployed by

In more detail

Full description

Conveyor reliability is a significant issue for any mining operation, and it costs companies millions in losses for unplanned downtime and damage. ContamID helps you avoid those losses by using AI to detect contaminants in real time.

The challenge

You need reliable feed stock into your mill to maximise production. Contaminants like bucket teeth and wood disrupt ore production – costing mining companies millions each year from unplanned downtime, maintenance and repair costs, and reduced throughput.

The approach

ContamID solves that. It’s an AI-based solution that identifies contaminants on a moving conveyor in real-time.

When it detects a contaminant, it can either alert an operator or trigger an automatic removal - preventing unplanned downtime, reducing maintenance and repair costs, and improving throughput.

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2022 Globee Award Winner

A bronze award in the 'Startup of the Year: Business Services' category