Masterclass: Transforming Chemical Data Gaps into Sustainable Product Decisions

Measured chemical data, such as emission factors and life cycle inventory (LCI) datasets, exists for only a small fraction of substances used across enterprise product portfolios.

With expectations for product-level transparency and audit-ready compliance continuing to rise, chemical product manufacturing engineers, sustainability and compliance teams are forced to rely on proxies, fragmented supplier inputs, and manual research. These approaches lead to inconsistent models, slower analysis, and reduced confidence in product-level decisions.

In this masterclass, Makersite’s experts will show how AI-assisted chemical modeling (ChemAI) closes chemical data gaps by generating transparent, traceable models from synthesis and pathway data, enabling continuous analysis and scalable, decision-ready insights across product stewardship, LCA, PCF, and compliance.

Join us to see how to move from fragmented data and manual effort to scalable, decision-ready insights across your product portfolio.

✅ Understand why chemical data gaps are a critical bottleneck for product stewardship, LCA, and regulatory compliance at scale

✅ Learn how AI-assisted modeling generates transparent, usable chemical models when emission factors and LCI data are unavailable

✅ See how to replace proxies and manual research with a more scalable, system-driven approach to chemical data

✅ Apply AI-assisted modeling (ChemAI) methods to enable faster, audit-ready decisions across product design, compliance, and sustainability workflows