Algo Grande Engages AI-Metals to Advance AI-Driven Integration Across Adelita Data; Reinforces Target Framework from Prior Geophysical Programs

13:30 Uhr  |  Newsfile

• Engaged AI-Metals to execute a 12-month AI-driven data reprocessing and integration program across the 100% owned Adelita Copper-Gold-Silver Project

• Multiple independent datasets converge on skarn-style copper mineralization proximal to intrusive-carbonate contacts, consistent with a large skarn-porphyry system

• AI-Metals platform will be continuously updated as new exploration data is generated through the ongoing drilling and field programs

• Results will be used to refine drill targeting and support a Phase 2 exploration program planned for late Q1-early Q2 2026

Vancouver, January 15, 2026 - Algo Grande Copper Corp. (TSXV: ALGR) (OTC Pink: KNDYF) (FSE: KM00) ("Algo Grande"), announces that it has engaged AI-Metals, an artificial intelligence and geoscience analytics company employing leading PhD AI researchers from the University of Cambridge, to undertake a 12-month AI-driven data reprocessing and integration program across its 100% owned Adelita Copper-Gold-Silver Project in Sonora, Mexico.

The engagement is a core component of Algo Grande's strategy to apply modern, data-driven exploration techniques to complex mineral systems by integrating historical and newly generated datasets to reduce exploration risk and refine drill targeting.

This work builds directly on the Company's recent announcement identifying 32 high-priority exploration targets, the commencement of a high-definition ground magnetic survey, and the development of a modern 3D geological model at Adelita. The AI-Metals platform will be continuously updated and refined as new geological, geochemical, and geophysical data are generated through ongoing drilling and field programs.

AI-Driven Data Integration and Target Confirmation

In collaboration with AI-Metals, Algo Grande has completed an integrated analysis that synthesizes:

• Airborne magnetic and electromagnetic (AEM) datasets
• Satellite-derived alteration indices (hydroxyl and ferric oxide signatures)
• Surface geochemical sampling
• Induced polarization (IP) chargeability and resistivity data

The objective of this analysis is to identify zones where multiple independent datasets spatially converge, thereby reducing ambiguity inherent in single-dataset interpretation and providing a more robust technical basis for target definition.

The AI-driven results support and enhance the target framework previously communicated, with coincident geophysical and geochemical signatures interpreted to be consistent with skarn-style copper mineralization developed proximal to intrusive-carbonate contacts, the same geological context underpinning the 32 high-priority targets announced previously (see news release from January 9^th).

Key Integrated Observations

The AI-driven integration of geological, geochemical, and geophysical datasets provides a coherent picture of a large, structurally controlled skarn-porphyry mineral system at Adelita. Independent datasets consistently converge along the same corridors, materially strengthening confidence in the Company's priority targets.

This integration highlights how surface alteration, geochemical anomalies, magnetic responses, and subsurface IP signatures overlap, suggesting vertical continuity from surface expressions into deeper potential mineralized zones. This multi-layered convergence significantly reduces single-dataset ambiguity and supports systematic drill targeting.

Figure 1. AI-driven clustering of integrated geological, geochemical, and satellite datasets across the Adelita Project

To view an enhanced version of this graphic, please visit:
https://images.newsfilecorp.com/files/11660/280479_1cf2af7cc41cb968_001full.jpg

• AI-driven clustering (figure 1) highlights geologically and geochemically distinct zones, with red clusters corresponding to interpreted skarn zones at Cerro Grande while green and yellow clusters reflect geochemically anomalous areas influenced by Cu-Au (green) and Cu-Mo (yellow) signatures.

Figure 2. Landsat false colour composite highlighting hydroxyl alteration, ferric oxide signatures, and magnetic contrast

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https://images.newsfilecorp.com/files/11660/280479_1cf2af7cc41cb968_002full.jpg

• Magnetic and electromagnetic data reveal structural and lithological boundaries interpreted to represent potential intrusive contacts and skarn fronts (figure 2). Blue-cyan zones correspond to areas of elevated hydroxyl alteration and magnetic response, consistent with copper anomalies at surface identified at Cerro Grande and along interpreted intrusive-carbonate contacts. Red to orange areas reflect oxidized surfaces dominated by ferric oxides, interpreted to represent oxidized caps or alteration halos of intrusive bodies.

Figure 3. 3D IP Chargeability Inversion over the Mezquital target

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https://images.newsfilecorp.com/files/11660/280479_1cf2af7cc41cb968_003full.jpg

• Large, coherent chargeable bodies below ~100-200 metres suggest potential sulphide mineralization, or a deep-seated fault system at depth (figure 3).

Taken together, these independent lines of evidence reinforce the interpretation of a structurally controlled skarn-porphyry system, consistent with the high-priority target corridors previously reported.

Integration with Ongoing Work

The AI-Metals analysis is integrated with the Company's broader technical work, including:

• Modern 3D reprocessing of historical magnetic, IP, and MT datasets (now completed)
• Ongoing high-definition ground magnetic survey over the Cerro Grande skarn zone
• Results from the Company's current oriented-core diamond drilling program
• Surface trenching, geological mapping, and systematic geochemical sampling

This combined dataset is being incorporated into a unified 3D geological and structural model to sharpen understanding of mineralization geometry, continuity, and controls, and to prioritize de-risked drill targets.

Exploration Implications and Next Steps

The AI-driven data integration materially enhances Algo Grande's understanding of the scale, architecture, and controls of the Adelita mineral system and reinforces the high-confidence targets identified in recent announcements. The Company intends to use this integrated model combining AI-generated prospectivity, detailed ground geophysics, and oriented core drilling to further refine drill targeting and support the design of a Phase 2 exploration program, which Algo Grande aims to commence toward the end of Q1 or early Q2 2026, subject to results and logistics.

ITG Engagement

Algo Grande also announces that it has entered into a market making services agreement with Independent Trading Group (ITG), Inc. ("ITG"), a CIRO-registered dealer member with a place of business in Toronto, Ontario, to provide market making services for the Company's common shares trading on the TSX Venture Exchange.

Under the agreement, ITG will use commercially reasonable efforts to maintain a liquid market for Algo Grande's common shares, including entering bids and offers on the TSX Venture Exchange in accordance with applicable securities laws and stock exchange policies. The objective of the engagement is to enhance market depth, support orderly trading, and improve overall liquidity for shareholders.

In consideration for these services, Algo Grande will pay ITG a monthly cash fee of $6,000, plus applicable taxes. No shares or options are being granted to ITG in connection with the engagement. The agreement is subject to automatic monthly renewal and may be terminated by either party upon written notice, in accordance with its terms.

ITG and its principals are arm's length to the Company and, to the knowledge of the Company, do not hold any securities of Algo Grande at the time of this announcement.

Qualified Person

The disclosure of scientific and technical information contained in this news release has been reviewed and approved by Lorne Warner, P.Geo., who is a "qualified person" within the meaning of National Instrument 43 -101- Standards of Disclosure for Mineral Projects ("NI 43-101"). Mr. Warner is responsible for the technical content of this news release. Mr. Warner is independent of the Company.

About AI-Metals

AI-Metals is a data science and geoscience analytics firm specializing in the application and creation of novel AI, Machine Learning, and heterogeneous dataset integration of complex geological systems. The company employs leading PhD data science and AI experts from Cambridge University along with industry partners to improve subsurface understanding, reduce exploration risk, and support predictive targeting.

About Algo Grande Copper Corp.

Algo Grande Copper Corp. is a growth-focused mineral exploration company advancing the Adelita Project, a district-scale, multi-system copper-gold-silver opportunity positioned in the prolific Arizona-Sonora copper belt.

The company is dedicated to unlocking the full mineral potential of this under-explored corridor through disciplined data-driven exploration, technical excellence, and a firm commitment to value creation for shareholders. The 5,895-hectare Adelita Project is anchored by the high-grade Cerro Grande Cu-Au-Ag skarn discovery, which exhibits strong continuity along a defined corridor extending over 6 kilometers. Reprocessing of legacy geophysical data and field mapping indicate the presence of a potential porphyry system at depth, suggesting a classic skarn-porphyry mineralization model similar to major deposits found throughout northwestern Mexico.

Cautionary Statement on Forward-Looking Information

NEITHER THE TSX VENTURE EXCHANGE NOR ITS REGULATION SERVICES PROVIDER (AS THAT TERM IS DEFINED IN THE POLICIES OF THE TSX VENTURE EXCHANGE) ACCEPTS RESPONSIBILITY FOR THE ADEQUACY OR ACCURACY OF THIS RELEASE.

This news release contains statements and information that, to the extent that they are not historical fact, constitute "forward-looking information" within the meaning of applicable securities legislation. Forward-looking information is based on the reasonable assumptions, estimates, analysis and opinions of management made in light of its experience and its perception of trends, current conditions and expected developments, as well as other factors that management believes to be relevant and reasonable in the circumstances at the date that such statements are made, but which may prove to be incorrect. Forward-looking information involves known and unknown risks, uncertainties and other factors that may cause the actual results, performance or achievements of the Algo Grande to differ materially from any future results, performance or achievements expressed or implied by the forward-looking information, including, but not limited to, statements relating to the proposed use of proceeds of the Financing, the registration of title to the Concessions, business development, results of operations, and those listed in filings made by Algo Grande with the Canadian securities regulatory authorities (which may be viewed at www.sedarplus.ca). Accordingly, readers should not place undue reliance on any such forward-looking information. Further, any forward-looking statement speaks only as of the date on which such statement is made. New factors emerge from time to time, and it is not possible for Algo Grande's management to predict all of such factors and to assess in advance the impact of each such factor on Algo Grande's business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward- looking statements. Algo Grande does not undertake any obligation to update any forward-looking information to reflect information, events, results, circumstances or otherwise after the date hereof or to reflect the occurrence of unanticipated events, except as required by law including securities laws.

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/280479