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Windfall Geotek Delivers Final AI-Driven Gold, Copper and Silver Targets on the Hi-View Resources's Toodoggone Projects, in North-Central British Colombia
Brossard, Québec - TheNewswire - May 14, 2026 — Windfall Geotek Inc. (CSE: WIN; OTCQB: WINKF) ("Windfall" or the "Company"), a pioneer in Artificial Intelligenc
About this update from Windfall Geotek Inc.
[{"type":"text","content":"Brossard, Québec - TheNewswire - May 14, 2026 — Windfall Geotek Inc. (CSE: WIN; OTCQB: WINKF) (\"Windfall\" or the \"Company\"), a pioneer in Artificial Intelligence (AI)-driven mineral exploration since 2005, is pleased to announce that they have used their Machine Learning AI System to provide high probability gold, copper and silver exploration targets over the Hi-View Resources’s Toodoggone Projects located in north-central British Colombia. The Toodoggone Projects are located within the highly prospective Golden Horseshoe of British Columbia’s renowned Toodoggone Gold & Copper District. The project span more than 27,000 hectares and is located near major discoveries such as Aurora & advanced development sites including Kemess, Ranch, Lawyers, Mets, Baker & Shasta. During the month of April 2026, hundreds of variables including magnetic and topographic data along with gold, copper and silver assay data, covering the HI-VIEW RESOURCES Toodoggone Project, were subjected to an AI evaluation by the Windfall Geotek AI System. WINDFALL GEOTEK generated a total of fourteen (14) gold targets, five (5) copper targets and thirty-one (31) silver targets across all of Hi-View Resource’s Toodoggone Projects, based on level of similarity of 80% - 85% of finding the same mineralized rocks. Four (4) areas located in Black Pearl, Lawyers East, and Saunders Properties are highlighted simultaneously by gold, copper and sliver AI Models. Michel Fontaine, President and Chief Executive Officer of Windfall Geotek, commented: \"A major challenge in mineral exploration is overburden that obscures surface geology. Windfall’s AI-driven platform addresses this by identifying shallow targets (0–300 metres) beneath swamps and ground cover where outcrops are not visible.Machine Learning does not create something new — it recognizes and reproduces geological patterns from existing data. By extracting the digital signature of a known deposit and applying it to historical datasets, our system identifies areas with similar mineralization potential.This approach can reduce the exploration footprint by up to 98–99%, enabling faster and more targeted exploration..\" Nader Mostaghimi Vice President Exploration and Nick Horsley Chief Executive Officer of Hi-View Resources, commented: \" We are very pleased with the results of the AI generated targ...