Customer Service Automation
Panasonic wanted to get closer to end customers, but service, commerce, logistics, and enterprise data lived across disconnected systems, limiting personalized support and making it hard to layer AI into customer service.
Reported impact
Panasonic accelerated development, reduced maintenance effort and support tickets, and created a connected service platform that can deliver more personalized, AI-assisted customer support across the region.
Panasonic built a customer-service foundation for Asia Pacific by connecting service, commerce, logistics, and enterprise systems through MuleSoft and then layering Service Cloud, Einstein for Service, Agentforce, and Data Cloud on top. MuleSoft now manages hundreds of interfaces across Salesforce, SAP, Panasonic's ecommerce environment, and third-party logistics providers so service teams can work from connected data instead of isolated applications. Customer inquiries and cases are handled in Service Cloud, where Einstein for Service drafts summaries, replies, and case wrap-ups, while Agentforce uses retrieval-augmented generation over structured and unstructured Data Cloud content such as product manuals and troubleshooting instructions to recommend the next best resolution. That architecture lets Panasonic give advisors a unified service console, reuse APIs faster, and continuously improve support quality without rebuilding the integration layer for each new channel or workflow.
Operational data from SAP, ecommerce, logistics, and Salesforce systems flows through MuleSoft into a shared service architecture, where cases are worked in Service Cloud and AI reads Data Cloud knowledge and product documentation to recommend or automate support actions.
It has also improved API management and governance, enabling a 70% reduction in maintenance effort and a 34% reduction in support tickets.
MuleSoft's unified integration, automation and API management platform supports 350 interfaces across systems, including Salesforce, SAP, and Panasonic's e-commerce and third-party logistics solutions.
Teams use Einstein for Service for AI-generated conversation summaries, service replies, and automated case wrap-ups.
The agent uses Retrieval Augmented Generation to search across structured and unstructured data in Data Cloud, including product manuals and troubleshooting instructions.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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