Abstract
Managing Rich Communication Services (RCS) in the context of business messaging campaigns involves navigating complex asynchronous message states, technical error codes, and delivery telemetry across diverse regions. Businesses that use RCS struggle to interpret raw logs which leads to increased support tickets for the provider and delays in troubleshooting. This disclosure describes techniques that address insight gaps that appear between observed errors and their root causes in tools that manage RCS for Business Messaging (RBM). Rather than simply report an error code, a large language model (LLM) is used to determine and display the likely root causes of the error. The LLM retrieves documentation and analyzes it alongside the specific traffic patterns around the error to explain why the error occurred. Troubleshooting is made contextual and business messaging tools are elevated from passive data presentation to active, grounded reasoning. Furthermore, to prevent cross-tenant data leakage through model memory, each session is stateless and the model context window is purged after every interaction.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Gandhe, Tania Pradeep, "LLM-powered Diagnostic Insights for Business Messaging via Rich Communication Services", Technical Disclosure Commons, (February 17, 2026)
https://www.tdcommons.org/dpubs_series/9332