Predictive Asset Monitoring
We use advanced pattern matching algorithms to continuously monitor thousands of assets to identify potential upset conditions prior to onset.
We optimize mining operations through scalable predictive maintenance and logistics optimization at the edge.
Earlier failure prediction lead-time through the automated discovery, extraction, and prediction of determinative spectral features to classify compounds and detect anomalies.
Full-package flexibly-integrated solution with either stand-alone hardware-software package or API integration.
Applied to mechanical systems without OEM integration or engineering change management.
Our Deep Reinforcement Learning solver tackles highly complex decision-making problems at scale, beyond the capability of traditional tools and without high-powered computing infrastructure.
Small changes to inputs can be re-calculated in seconds making it easy to quickly test different scenarios.
Our solutions can operate on local systems, ideal for environments with unreliable connectivity and providing continuity and performance at the edge.
Increase Uptime
Reduce Site OPEX
Increase Production Revenue
Reduce Emissions Profile
Improve Safety
We use advanced pattern matching algorithms to continuously monitor thousands of assets to identify potential upset conditions prior to onset.
We use advanced causal machine learning algorithms to uncover the root causes of asset performance.
We use domain-informed foundation model adaption to classify fault pre-cursor states from spectral data representations.
We use advanced pattern matching algorithms to continuously monitor thousands of assets to identify potential upset conditions prior to onset.
We use advanced causal machine learning algorithms to uncover the root causes of asset performance.
We use domain-informed foundation model adaption to classify fault pre-cursor states from spectral data representations.
Level 6, 74 Pirie Street Adelaide SA 5000 Australia
+61 429 455 406
