(untitled) (PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE KYRGYZ REPUBLIC, 2026)
To develop and describe an electronic service for automated cost estimation in mining projects accounting for equipment types, productivity, fuel, maintenance and rental costs. We propose a modular architecture with data import, parameterized computation, and report generation.. Compared to manual workflows, the approach reduces time, minimizes errors, and increases cost transparency. The rationale builds on ongoing digitalization trends in mining and on Python/ML methodology.