Non Destructive Asbestos Detection
Scaling innovative microwave sensing and AI powered technology to deliver a safer Australia through real time identification.
"Currently, about 125 million people in the world are exposed to asbestos at the workplace. Approximately half of the deaths from occupational cancer are estimated to be caused by asbestos."
World Health Organisation, 2018.
>99%
Detection Accuracy
< 1s
Identification Speed
CRC-P R18
Funding Awarded
The Challenge
Traditional asbestos testing presents significant technical limitations. The process is destructive, requiring physical samples to be cut and removed, which is slow, hazardous, and costly. This creates major bottlenecks in renovations, demolitions, and disaster recovery efforts.
In 2021, under the BRII RegTech Round, we were awarded funding to develop an innovative solution that could provide non destructive, accurate, and immediate results, meeting stringent regulatory requirements.
Project Evolution
2021: BRII RegTech Feasibility Study
Initial research and development funding awarded to solve regulatory bottlenecks in asbestos identification.
2023-2024: BRII RegTech Proof of Concept
Handheld hardware developed and machine learning models validated on lab samples.
2025: CRC-P R18 Grant
Major federal grant awarded for partnership scaling and real world deployment.
Microwave Sensing & Machine Learning
Our solution combines low power microwave signals with sophisticated machine learning. By measuring the unique dielectric properties of building materials, our model identifies the presence of asbestos in less than a second, moving organisations beyond theory into tangible safety impact.
Data Collection
Materials are scanned and cross referenced with lab verified reference libraries.
Model Training
Advanced analytics bridge the gap between business strategy and real world implementation.
Deployment
Handheld devices are deployed for real time safety identification in the field.
HARDWARE INTERFACE SIMULATION
READY
Device Ready
Press button to start scanning.
Strategic Partnerships
Predictive Analytics Group
Advanced analytics implementation, strategic lead and AI governance.
Strickland Labs
Specialist engineering and material science innovators.
QUT
Academic research excellence and technical validation partners.
Efficiency Breakthrough
Traditional Lab Method
- 15 Mins: Destructive sample collection.
- Hours: Transport to a certified lab.
- Several Hours: Lab analysis and reporting.
Standard Wait Time
~24 Hours
PAG Handheld Device
- Under 2 Minutes*: Complete sampling process.
- Under 1 Second: Automated identification and reporting.
PAG Speed Benchmark
Under 2 Minutes
*Proven by our Proof of Concept PoC
Winning the CRC-P R18 Grant represents a massive leap forward. This federal funding fuels our mission to deliver a safer Australia by combining cutting edge sensors and advanced analytics and tangible implementation to develop real time detection devices.
Our collaboration with Strickland Labs and QUT allows us to bridge the gap between business strategy and tangible implementation. We empower organisations to move beyond theoretical applications and create real world impact that saves lives.
Accuracy Comparison
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