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Upon receiving the exploration data, the VRIFY team will conduct a careful review, QAQC and compilation process. Once these steps are completed, the data will be validated with the technical team from the participating company. This step is crucial in making sure the data is of the highest possible quality. Most often, this initial compilation is the longest and represents 80% of the amount of work to be conducted. Depending on the level of organization of the transferred data this can take from days to weeks. Once the data is compiled and transferred to the proper formats, the training and predictions usually take a few days. Once the model is trained, updates can be made within a few hours of receiving new data.

Now is the time to consider AI targeting across all stages of mining, from initial exploration through resource delineation to the management of operating mines. Leveraging AI for informed decision-making can save millions in exploration costs and significantly reduce wasted time at every phase. Swift project development and rapid resource delineation, underpinned by AI at each step, benefit financial outcomes, environmental sustainability, and local communities alike.

While human geologists excel at recognizing patterns and leveraging their expertise for predictions, their ability to process complex patterns is typically limited to 3 to 4 layers of information simultaneously. Beyond this threshold, the effectiveness in identifying patterns diminishes greatly. Machines, by means of statistical models, however, can analyze data across multiple dimensions and utilize their flawless memory to reference previously encountered information. Artificial Intelligence (AI) learning models significantly enhance our capacity to scrutinize geologic data, offering more objective, unbiased and result-driven predictions. AI not only supports the validation of existing targets but also identifies potential ones that might have been overlooked. This heightened accuracy in target identification reduces the likelihood of drilling unnecessary holes, thereby improving the overall chances of successful discovery. Furthermore, by incorporating both positive and negative results from exploration work, the model can benefit from both successful holes and misses.

No, your data remains confidential throughout the process. Models are developed specifically for each asset, using training data exclusively sourced from the individual client's dataset. While our transformer components benefit from enhancements driven by collective data in a latent space, your specific data is never exposed or accessible to any other parties through the models. As new features are identified by our models, they are stored and utilized to refine and enhance the predictions in future iterations of the model, ensuring continuous improvement without compromising data privacy.

For AI and machine learning models, having a larger dataset generally leads to better outcomes. However, challenges can arise from datasets that are sparse or lack overlap, potentially affecting the model's accuracy. VRIFY evaluates the available data volume prior to entering any agreement, ensuring that we can deliver actionable, data-driven insights that truly benefit our clients.

VRIFY AI operates by merging the capabilities of large language model transformer architectures (vit) with machine learning classification techniques to produce probabilistic representations of the Earth in three-dimensional space. A prediction for the VRIFY Prospectivity Score (VPS) is made by training a supervised learning model from the existing mineral occurrences and embedded data space. For each area of interest, data is compiled, and a uniquely fine-tuned and optimized model is developed, tailored to the specifics of the available information.