MaterialsZone, a materials informatics platform providing AI-Guided R&D for Materials Innovation, announced the launch of Maven, a conversational AI interface powered by its Enterprise Materials Knowledge Center. Designed for R&D and production teams, Maven adds a powerful new capability to the MaterialsZone platform, providing a faster, more intuitive way to access and explore materials knowledge across the organization. By connecting internal and external data sources, Maven helps teams understand what has been tried, what works, and where to look next. By combining analytic AI and GenAI, Maven strengthens MaterialsZone's ability to deliver high-quality R&D and optimize the workflows and processes that drive materials innovation.

A recent MIT study found that 95 percent of enterprise AI initiatives fail, often due to the inability to securely connect AI to proprietary systems and data. Among all sectors tracked, Energy and Materials ranked last, with researchers describing "near-zero adoption; minimal experimentation." The study states that "the core barrier to scaling is learning", and that "most GenAI systems do not retain feedback, adapt to context, or improve over time." Maven brings together GenAI's conversational interface and MaterialsZone's proprietary analytic AI, giving users an intuitive way to query and reason across the organization's accumulated materials knowledge.

"Across the organizations we work with, there's often the one expert, the person people naturally come to for advice and insight, who's been around for decades and remembers every experiment, every formulation, every result," said Ori Yudilevich, CPO of MaterialsZone. "Maven captures that expertise and makes it accessible to the entire team through a simple conversation. It works because it's built on your data, secured in your environment, and respects your permission structures."

Built into the MaterialsZone platform, Maven adds a large language model layer on top of the company's existing data management and machine learning engine. With no code required, users can analyze data, generate visualizations, surface past experiments, and compare internal results against external sources like technical data sheets, supplier databases, and published literature, all through natural conversation.

Maven's impact extends beyond the lab, connecting scientific inquiry to faster, more informed business decisions. Researchers reported spending up to 60 percent of their time on knowledge retrieval tasks, fielding internal questions, locating past experimental results, and verifying tested formulations. Maven handles those queries instantly, freeing researchers to focus on actual discovery. It also puts critical knowledge directly into the hands of Sales and Procurement teams. Sales teams can respond to customer specification requests immediately, and Procurement teams can evaluate raw material substitutions against formulation requirements, without waiting on R&D.

Data security is a top priority at MaterialsZone, and Maven is built to reflect that. Customer data is stored exclusively within that customer's secure environment and is used only to train their proprietary models. The platform never uses that data to train shared models, and no organization can access or benefit from another's information. A granular permission system adds a further layer of control, ensuring users see only what they're cleared to see.