Booth 3145

At Natural Products Expo West, Clean Label Project, a nonprofit focused on transparency and safety in consumer products, will launch a new mobile app and Purity+Ai, an AI platform designed to help brands validate purity and consumers make safer purchasing decisions.

Mobile App

The new Clean Label Project app will give shoppers direct access to Clean Label Project’s certification data and product insights on a mobile interface. The app enables users to discover certified products; access brand and product purity information; make smarter purchasing decisions; and support brands committed to higher safety and transparency standards.

“Science only matters if it’s accessible,” said Molly Hamilton, executive director. “With our new mobile app, we’re delivering the names of products that have undergone rigorous testing and evaluation directly into consumers’ hands. Transparency should not be buried in fine print. It should be available in real time, empowering families to make informed decisions backed by science.”

Clean Label Project has tested thousands of consumer products for heavy metals, pesticide residues, plasticizers, and other industrial and environmental contaminants. The new mobile experience puts that information directly into consumers’ hands at point of purchase.

Purity+AI

Purity+AI is a new platform for brands that believe they offer the highest-quality, cleanest products.

It leverages:

• The largest CPG industrial and environmental contaminant database
• Data from thousands of product samples and millions of contaminant-to-ingredient linkages
• Advanced AI modeling to identify ingredients associated with contamination risk
• Root cause analysis capabilities to help brands proactively reduce exposure risks

“Purity+AI transforms thousands of laboratory-tested samples and millions of contaminant data points into actionable intelligence,” Hamilton said. “By identifying ingredient-level contamination patterns and enabling root cause analysis, we are giving brands the scientific insight they need to proactively reduce risk. This is about moving from reactive testing to predictive transparency.”