Customer Service Automation
Paychex needed a secure way to help call-center representatives answer complex payroll, benefits, and compliance questions faster while using feedback and interaction data to keep improving its new AI assistant.
Reported impact
Paychex improved representative productivity and response quality, created a differentiating client experience, and built an AI feedback loop that can keep improving accuracy as the company expands its cloud-first strategy.
Paychex launched a beta AI copilot for HR service representatives on MongoDB Atlas so it could unify knowledge retrieval, feedback capture, and model improvement in one operational store. The assistant uses ChatGPT to query a broad knowledge base that combines call and chat transcripts, internal databases, government regulations, and third-party datasets, helping representatives answer complex customer questions more accurately. Atlas stores the generated interaction data and user feedback so the team can iteratively retrain the out-of-the-box models, measure adoption, analyze question patterns, track response speed, and build visualizations for continuous tuning. The system also preserves conversational context to improve follow-up answers. Atlas was deployed in less than a month, and Paychex can roll out full datasets across data centers in as little as 27 minutes while keeping MongoDB central to its cloud-first AI strategy.
Call and chat transcripts, internal data, government regulations, and third-party datasets are queried through ChatGPT, the responses and feedback are stored in MongoDB Atlas, and Paychex uses that stored interaction data to retrain models, build visualizations, and improve future answers.
When users provide feedback on the quality of the model’s responses, the data stored in MongoDB Atlas iteratively retrains the out-of-the-box models.
“Anything you can do to help make individual workers more productive is a win.”
“As with anything that’s data driven, the quality of the data and how it’s curated to solve the problems at hand will define the results.”
“We’re using our AI copilot solution, powered by MongoDB Atlas, to better support our service representatives.”
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.
Related
ServiceNow Sample Modified · Oct 7, 2026 Thrive Thrive needed a scalable managed-services operating model for SMB customers, but repetitive ticket triage, fragmented customer collaboration, and slow access to context were consuming engineer time and delaying issue… 21,000 hours of manual work saved AI Agents · AI Control Tower · Action Fabric ServiceNow Sample Modified · Jul 30, 2026 Auxilion Auxilion needed a service-management operating model that could integrate customer environments, rationalize data trapped in legacy systems, and scale managed support for SMB customers without adding more complexity,… 100% platform reliability Technology Provider Service Management ServiceNow Sample Modified · Jul 30, 2026 CANCOM CANCOM had siloed data across ERP, CRM, SharePoint, and other systems, which made it hard to streamline services, reuse enterprise knowledge, and give employees or customers consistent support. 1 Unified platform supporting all internal services Now Assist · ServiceNow AI Platform · ServiceNow portal 
