🚀 SYSPRO's AI Move for the Manufacturing Sector: Torque AI!

[Elif Özaksu]

🛠️ Revolutionizing the Manufacturing Floor: SYSPRO Torque AI​

SYSPRO has introduced its new artificial intelligence platform developed specifically for the manufacturing sector: SYSPRO Torque. This industrial AI automates complex decisions on the manufacturing floor while prioritizing transparency and operator control.

🔍 Transparency and Reliability at the Forefront​

Torque not only identifies operational issues and suggests solutions but can also automatically implement approved actions across existing manufacturing systems. Designed with a "Glass House" principle, the platform ensures that every automated action is transparent, fully recorded, and explainable. Operators can easily track the business rules and data sources influencing a decision. This eliminates the risks associated with "black box" AI tools and provides a strict chain of justification for audits.

🔗 Integration and Ease of Use​

Built on nearly fifty years of manufacturing knowledge, Torque can natively connect to any ERP system, as well as legacy SCADA, MES, and warehouse management systems. This eliminates the need for custom middleware. The platform's no-code interface allows operational teams to define workflows in simple language. This means even non-technical personnel can quickly create and deploy AI agents.

💰 Value-Driven Pricing​

Torque provides measurable value by offering upfront cost estimates for each workflow. Operating with a usage-based, deterministic pricing model, the platform scales as automated agents prove their return on investment.

💡 Increasing AI Confidence in Manufacturing​

The manufacturing sector has been hesitant to use autonomous AI directly on the production floor due to concerns about trust and reliability. An incorrect automated decision can lead to serious cascading errors in areas such as inventory forecasting, supply chain logistics, planning, and costing. Platforms like Torque address the need for data governance and operational safety in mid-market manufacturing by prioritizing explainable AI and deterministic rule execution. Furthermore, by allowing domain experts like production managers to create AI agents using natural language instead of data scientists, it bridges the gap between advanced machine learning capabilities and the realities of the daily manufacturing floor.