Future Fuels has entered a partnership with VRIFY Technology to enhance its uranium exploration efforts in the Hornby Basin in Nunavut, Canada.
The collaboration aims to refine and validate Future Fuels’ exploration plans through advanced AI and predictive modelling.
This strategic move aligns with the company’s multi-phase exploration strategy, which includes data digitisation, geologic modelling and remote sensing to improve understanding of the basin.
Spanning more than 3,400km², the Hornby Basin is now under the unified control of Future Fuels.
The company holds 232 mineral claims and six mineral leases, positioning it to explore and develop the region as a high-potential uranium district.
Future Fuels will deploy DORA, VRIFY’s AI-assisted mineral discovery platform, to accelerate discoveries and reduce exploration timelines.
The partnership involves comprehensive data aggregation and the use of AI methodologies to extract insights from various data sources including historical reports, drilling data and regional geophysics.
VRIFY’s feature processing products have enabled the enhancement and expansion of existing datasets, creating approximately 60 additional products to aid in predictive modelling.
This technology streamlines data compilation and generation, allowing Future Fuels to focus on planning its exploration programme, which may include AI-generated target ground truthing, infill drilling, geophysical surveys and permitting activities.
DORA offers real-time, iterative predictive modelling with an intuitive interface for integrating multi-dimensional datasets. It generates a VRIFY Prospectivity Score, providing data-driven confidence for exploration.
Future Fuels’ exploration team will use DORA to detect high-potential mineralisation zones and enhance the accuracy of predictions.
Through this partnership, Future Fuels gains access to DORA’s web-based modelling environment and advanced training from VRIFY to build, refine and validate predictive models. This hands-on approach supports continuous discovery and performance optimisation.
