Resource models; Grade control; Mine planning
StratumAI applies AI modeling to mining operations. The official site identifies resource, grade-control, geometallurgical and geotechnical models as areas of work. Its approach uses multivariate geostatistical information to support decisions where data density differs across a site. Published examples include drill targeting, reconciliation and error correction during production or brownfield expansion. These are modeling and decision-support capabilities, not a guarantee of mineral value or an assurance that a mine plan is correct. Geological interpretation and operational choices remain dependent on the available data and qualified review. The listing therefore avoids repeating promotional claims about increased mined grade as expected outcomes.
StratumAI is best described as Environmental Analysis Tool for environmental teams, infrastructure operators. The practical workflow centers on resource modeling, grade-control modeling, geometallurgical analysis, drill-target support, reconciliation workflows. Users normally bring their own task context into the product and review the resulting output before relying on it.
Useful use cases include resource models, grade control, mine planning. Category placement is kept to Environmental Analysis because the tool should be listed where people would actually compare it. Supported access is recorded as the access model described by the product, and integrations are limited to connections that could be verified from the available source material.
The developer is recorded as StratumAI. Pricing is listed conservatively as Current pricing should be checked on the official site. Free-plan status is recorded as Free access terms are not clearly established in the reviewed public material. Before using StratumAI for production work, check the current plan page, account limits and any commercial-use terms that apply to the files, data, media or decisions involved.
Run a small real task first and compare the result with the original material. For generated text, media, code, analysis or operational actions, review factual claims, permissions and handoff steps before publishing or applying the output. This keeps the listing useful without adding unsupported benchmarks, invented model names or broad legal promises.
