Cut costs up to $2.5M per 1000 agents

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Leading US contact center operator faced long case resolution times, poor customer service experience, inefficient resource management. In addition to inefficient and expensive case solutions, the new client on-boarding process was not quick.


Analyze using ML the current and historical case data to make the most appropriate resolution recommendations, task routing and assignment decisions. Classify tickets by priority and ensure critical tickets receive attention. Recommend resolutions based on prior knowledge, always improving over time. Check for cases already open under different ID for the same customer to check if they can be grouped based on case data reducing case hops. Recommend the most appropriate engineer(s) to reduces case hops.


saving per 1000 call center agents
6 different areas improved in customer care and service
Onboard new BPO clients in 15 days or less