Opacity within raw material value chains obscures critical cost, carbon, and risk data, leading to suboptimal decision-making and weakened supply chain resilience. Existing tools often rely on aggregated metrics and lack the granular visibility required to address these challenges effectively.

AtmosphericAI offers an AI-first platform that constructs a dynamic digital twin of raw material value chains, providing facility-specific data from origin to factory gate. This digital twin quantifies and interconnects cost, carbon emissions, and risk at each stage of the supply chain. By delivering multi-tier visibility and actionable insights, the platform empowers procurement, sustainability, and supply chain management teams to optimize sourcing strategies, achieve carbon reduction targets, and enhance overall supply chain robustness.

AI-driven platform generating a dynamic digital twin of raw material value chains.

Granular, facility-specific data on cost, carbon emissions (CO₂e), and risk factors.

Multi-tier supply chain mapping with node-level visibility.

Predictive analytics for risk identification and proactive disruption mitigation.

Tools for category management, including should-cost analysis and supplier performance benchmarking.

Bottom-up carbon metric calculation for accurate Scope 3 emissions reporting.

Integration of lifecycle assessment data for sustainability reporting.

Real-time alerts and actionable recommendations for strategic sourcing and risk mitigation.

The platform targets global manufacturing businesses with complex supply chains, specifically procurement leaders, sustainability officers, and supply chain management professionals.

Revenue is generated through a SaaS model with tiered subscription plans based on usage and feature access.