Surgere, the industry leader in creating supply chain visibility with 99.9% data accuracy, has unveiled its new agentic AI assistant: Sophia, an intelligent supply chain companion fully integrated into Surgere's Interius platform. Sophia makes supply chain professionals' lives easier by delivering real-time analysis and action based on their unique supply chain data. Meet Sophia at Manifest 2025 at Booth #527.
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Surgere's agentic AI approach can perform tasks independently, adapt to new information and work with humans to solve problems. As part of Interius, Sophia allows human expertise and digital velocity to work side-by-side, enhancing speed-to-decision based on volumes of data-informed insights.
Deeply integrated into Interius, Sophia instantly offers customers with four validated use cases that are common to worldwide manufacturers, including:
- Automated Lane Assignments. Save time and reduce conflicting data and human error with automatic shipping lane processes, enabling accurate and efficient truck loading based on customer orders.
- Surplus Asset Location. Keep manufacturing, shipping, and operational processes running by identifying and reallocating raw materials, finished goods, and other inventory to where it is most needed throughout your network of locations.
- Automated Stop Shipments. Optimize supply channels with automated adjustments based on customer supply requirements; eliminate unnecessary shipments and associated labor.
- Takt Time Monitoring. Balance production supply and demand, allocating labor and resources to the best possible task.
"We've integrated AI into our platform to help process the mass volume of data that supply chains generate," said Nykaj Nair, Chief Revenue Officer, Surgere. "Humans simply cannot understand, process and act on data instantaneously-but AI can. We've seen these four use cases in every manufacturing environment from automotive to industrial to food and beverage, and Sophia monitors and responds to these and other critical data points that if left unaccounted for would cause significant friction for supply chain and logistics teams.
"We've enabled Sophia to learn a customers' unique supply chain data and become more powerful over time, but these four use cases allow our customers to deploy AI elements from Day 1. As we learn more about each unique supply chain's data, we can build more agentic AI solutions and use cases alongside our clients," Nair continued.
Sophia is fully integrated into Surgere's industry-leading foundation of 99.9% data fidelity, structured specifically for AI processing. Accurate data is the only way to enable AI within complex manufacturing and supply chain environments to create and identify meaningful insights. Without it, insights produced will be inaccurate, leading to suboptimal human decisions and poor business outcomes.
Sophia joins Surgere's complete platform of IoT visibility solutions for every manufacturing environment, including supplier networks, manufacturing facilities, warehouses, distribution centers and retail spaces.
For a demonstration of the power of Surgere's new AI, visit Surgere at Manifest 2025 at Booth #527 or visit www.Surgere.com to request a demo.
At Surgere, our singular focus is building AI-powered, frictionless supply chains that will change the world. We deliver on this mission with leading-edge, engineered IoT-based solutions matched seamlessly with our SaaS platform, Interius. The result for our customers is real-time, end-to-end visibility in critical supply chain operations: packaging specification management, asset management, asset localization, production control, inventory control, warehouse management, and transportation visibility. On a global scale, these results are powered by 99.9% differentiated data accuracy and amplified by an agentic AI approach that provides predictive insights to keep our customers ahead of supply chain disruptions and their competitors. Visit us at www.Surgere.com For more information, contact Michael Schwabe at Michael.Schwabe@surgere.com
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Contacts:
Michael.Schwabe@surgere.com
