Verusen launched its Explainability AI Agent for data and context-driven material and inventory optimization. This first-of-its-kind capability delivers transparency into Verusen’s stocking policy recommendations, enabling procurement, operations, and supply chain teams to trust, understand, and act on AI-driven insights.
“Too often, enterprise AI is a black box—something you’re expected to trust without question,” says Ross Sonnabend, chief product officer at Verusen. “Our Explainability agent changes that. We’re explaining the ‘why’ behind the recommendations and decisions in a language our users can understand, proving that responsible AI can be powerful and transparent.”
Key takeaways:
· Verusen’s Material Graph — dubbed as the world’s largest MRO materials knowledge base — has ingested over 41 million unique SKUs, $12 billion in annual inventory and spend, and all associated transactional POs.
· By integrating large language models (LLMs), machine learning, and natural language processing technologies, Verusen transforms manual, disconnected inventory management practices into streamlined, context-rich optimization strategies.
· Verusen’s Explainability Agent is task-driven to deliver clarifications and explanations to users. It examines model inputs, outputs, and logic to surface tailored insights directly within the platform, ensuring every decision is rooted in transparency and context.
· Key pillars of Verusen’s responsible AI design include:
No exposure of customer data to third-party LLMs;
Built-in explainability, not bolted-on as an afterthought; and
User-in-the-loop feedback models that improve recommendations over time.
