Cleo’s Transaction Monitoring & Management Solution Now Equipped with AI-Powered Technology

“Intelligent error categorization” is an AI-powered approach to how integration errors are identified, categorized, and resolved for its managed services customers.

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Cleo announced advancements in its EDI managed services solution, Transaction Monitoring & Management (TM&M), with a new capability called “intelligent error categorization,” an AI-powered approach to how integration errors are identified, categorized, and resolved for its managed services customers.

“Cleo has a history of leading the market with many ‘firsts’ for delivering supply chain orchestration solutions,” says Mahesh Rajasekharan, president and CEO of Cleo. “We were the first provider to put EDI and API integration on the same cloud platform; the first to bring control to our customers with the choice of self-service, managed services or blended services business models; and the first to empower both technical and operational personas with end-to-end business process visibility. It is fitting that we are the first to revolutionize the market with an AI-powered managed services solution, closing the loop to optimize end-to-end supply chain workflows.”

Key takeaways:

 

·        Cleo’s TM&M offering combines the power of CIC, the Cleo Network, and managed services to deliver a technology-enabled EDI managed service.

·        This AI/ML-driven solution automatically classifies, analyzes, and consolidates integration errors—dramatically accelerating troubleshooting and resolution processes. The result is a technology-driven EDI managed service powered by AI and with what is said to be the industry’s fastest response time service level agreement (SLA) when compared to traditional people-driven “black-box” EDI managed services.

Cleo’s AI-powered TM&M service can deliver even better results than previously imagined, including 96% of all errors are automatically resolved without human intervention, allowing Cleo managed services and support teams to focus on higher-priority tasks; 75% of integration errors are automatically analyzed and grouped into actionable clusters; and exceeding the industry’s only 1-hour human response time commitment by over 12%.

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