Challenge: As a growing sustainability startup focused on metals, mining, and industrial supply chains, beSirius set out to establish visibility in AI-generated answers used by sustainability and compliance teams.
Rather than trying to appear across all sustainability topics at once, the team deliberately started with a niche and highly relevant area: Responsible Minerals Initiative (RMI) templates, including CMRT, EMRT, and AMRT.
These templates are widely used and frequently referenced in AI-generated research. While AI assistants regularly explained RMI requirements, beSirius was rarely mentioned or associated with these sustainability workflows.
Outcome: With iGEO, the team identified where and why competitors were referenced more often and made targeted improvements to how AI systems explain, compare, and rank sustainability and compliance tools. Within 90 days, LLM mentions increased from 177 to 621, AI visibility reached 50%, and beSirius moved from #6 to #2 in AI-driven rankings.
Sustainability teams in metals, mining, and industrials increasingly rely on AI assistants to understand reporting requirements, complete RMI templates, and evaluate digital tools that can support responsible sourcing and disclosure. When beSirius began working with iGEO, AI-generated answers covered these topics but rarely referenced beSirius. Competing tools were cited more frequently in explanations and comparisons.
beSirius partnered with iGEO to improve how AI systems associate compliance automation workflows with their platform. The objective was clear: ensure beSirius is surfaced when teams ask how to automate reporting and manage regulatory requirements at scale.
iGEO analyzes how AI assistants explain compliance processes, compare tools, and recommend solutions across high-intent queries. It identifies where beSirius is missing, under-ranked, or incorrectly positioned, enabling the team to make targeted improvements to AI visibility and ranking.
Within 90 days, beSirius achieved measurable gains:
Today, beSirius is consistently referenced in AI-generated sustainability guidance for RMI templates, strengthening its presence at the research and evaluation stage where sustainability teams form shortlists and select tools.
