Transforming Customer Support with R-CaseAssist: Scalable Automation Through AI

Driving Efficiency with AI

A HiTech company was facing rising operational costs and delays in customer support due to manual case handling and underutilized unstructured data. These inefficiencies were affecting service quality and customer satisfaction. To resolve this, Relanto proposed R-CaseAssist, an AI-powered solution using NLP and reasoning engines to automate triaging, optimize ticket routing, and deliver faster, more accurate support while ensuring compliance and scalability.

Key Issues Impacting Support

  • Overdependence on Manual Resolution: Heavy reliance on manual efforts for L1/L2 support created inefficiencies, slowed triage, and led to delays in resolving customer issues.
  • Inability to Leverage Unstructured Knowledge: Support teams lacked the tools to extract relevant information from manuals, help files, and documents, limiting context-aware responses.
  • Lack of Intelligent Routing and Classification: Cases were not routed or categorized intelligently, resulting in misdirected tickets and inconsistent prioritization.
  • Minimal Automation for Repetitive Issues: Repetitive queries consumed significant human effort due to limited automation, increasing support load and operational costs.
  • Support System Not Scalable with Demand: As support volumes grew, existing workflows failed to scale, leading to backlog, slower resolution, and potential drops in customer satisfaction.

Scaling Intelligent Support

  • Primary Goal: Enhance support efficiency and user experience by automating L1/L2 case resolution using NLP and reasoning-based systems.
  • Secondary Goal: Drive cost optimization and continuous improvement by reducing manual intervention and leveraging AI-powered decision-making.

R-CaseAssist: Proactive Support

To address the client's operational challenges, Relanto proposed R-CaseAssist, a modular AI-powered solution designed to automate and optimize customer support workflows. At the core of the solution is CaseAssist, an NLP-driven conversational agent that accelerates case resolution by handling routine queries and facilitating intelligent triaging.

The solution is further enhanced by a ReACT-based reasoning engine, enabling structured, multi-step resolution for L1 and L2 cases. It maintains contextual memory and leverages multiple knowledge bases using advanced techniques such as Retrieval-Augmented Generation (RAG), NL2SQL, and API/action pipelines, ensuring accurate and scalable automation with human oversight.

To improve ticket handling, machine learning models were deployed for classification, auto-routing, and identifying left-shift opportunities. Additionally, a custom analytics engine was integrated to deliver natural language-driven insights and reporting, supporting data-informed decision-making.

The system’s modular architecture allows seamless integration with existing platforms and supports the expansion of agentic tools and API layers, ensuring long-term adaptability and scalability across support operations.

Ops & CX Gains with R-CaseAssist

  • Intelligent Triage Reducing Resolution Effort

Automated case handling and smart triaging minimized manual workload, accelerating issue resolution

  • Operational Efficiency Through Automation

Reduced manual interventions led to streamlined workflows and improved overall support productivity.

  • Data-Driven Decision Making with Real-Time Insights

Custom analytics enabled natural language-driven reporting, empowering faster and more informed decision.

  • Enhanced User Experience and Retention

Faster, more accurate responses and real-time ticket tracking significantly improved customer satisfaction and loyalty.

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