About
Solar asset intelligence, grounded in engineering.
Founded in Sydney by Harrison Su, Sohan Domingo and Laurens Willems van Beveren, Metrifield Dynamics brings together expertise in materials science, field inspection and edge AI to connect multiscale diagnostics with energy performance and lifecycle economics.
Founders
The team
Harrison Su
Co-founder
PhD in Materials Science and Engineering, and Director of Caelvox Technologies, developing advanced sensing architecture and automated inspection systems. His research spans energy materials, terahertz imaging and advanced characterisation, including collaborations with the Australian Synchrotron and the Australian Centre for Neutron Scattering. Focused on translating materials science into validated diagnostics, industrial applications and asset intelligence.
Laurens Willems van Beveren
Co-founder
PhD in Applied Physics and Director of Australian Vacuum Services and Australian Drone Surveys. Former Experimental Scientist at Australia's National Measurement Institute, with extensive experience in precision instrumentation, measurement science and systems engineering. Combines scientific instrumentation expertise with CASA-credentialed drone operations, supporting reliable sensor integration, field inspection and engineering deployment.
Sohan Domingo
Co-founder
Master of Software Engineering, and Founder of Edgegenix, a Sydney venture studio building computer vision and edge inference systems for councils, utilities, fire agencies and insurers, and of BrightBuddy, bringing companion robots to Australian aged care. Long-standing focus on LiDAR, computer vision and geospatial systems for infrastructure, with hands-on experience across aerial data systems and edge AI deployment.
How we work
Decisions first, then measurements
Start where the money is
Engagements begin with cleaning schedules and targeted repair and replacement decisions on operating plants, then extend to manufacturing feedback and new-plant design.
Measure what we claim
Value is benchmarked against existing workflows on total expenditure per MW, delivered MWh and net lifecycle cost improvement, including deployment, inspection, computing and model-maintenance costs.
Share models, keep calibration local
Model structures and statistical priors are shared across sites; calibration, service costs and site constraints stay local.
Built with Edgegenix
Edgegenix contributes the edge AI: training the detection models and deploying them on the field platform.