45 Figure 1 - Comparison of legacy-data derived resource distribution to expert-specified analysis range. We next assessed the performance of AI-guided drilling optimization relative to conventionally targeted drilling. The AI system generated drill targets in batches of four, consistent with the operational constraints of a four-rig drilling program. Drilling under the AI-guided strategy was terminated once the resulting resource model was determined to be statistically comparable to the resource model derived from the full set of conventionally targeted drill holes. This condition was met after the AI-guided program had targeted drilling meters equivalent to approximately 45% of the total footage drilled in the conventional campaign. Figure 2 compares the resource models generated from the original legacy dataset, the AI-guided drilling campaign, and the conventional drilling campaign. The resulting P10, P50, and P90 ore shells exhibit a high degree of consistency across all three models, with differences in total low-grade volume of approximately 6%. Figure 3 presents the corresponding resource distributions, indicating that the AI-derived model reproduces the central tendencies and uncertainty bounds of the conventional model within reasonable tolerances. Taken together, these results suggest that AI-guided drilling optimization can achieve comparable levels of resource characterization while substantially reducing drilling requirements. At the project scale, this reduction translates into meaningful decreases in capital expenditure, development timelines, and exposure to exploration risk, with potential implications for both project economics and portfolio-level capital efficiency.
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