Case studies / 08
CX system for auto Issue root-cause analysis & Resolution
- 40%
- Reduction of Mean Time to Resolution
Mandate
For organizations handling large volumes of customer cases, create a unified CX intelligence layer that could connect fragmented conversations, account history, service activity, and operational events. The system needed to shorten investigation time, expose recurring issues, and guide support teams toward the most effective resolution while protecting service quality and customer trust.
What Was Built & AI Role
Developed a customer issue intelligence platform that consolidated interactions across channels and linked each case to the relevant customer, product, and operational context. It detected recurring patterns, surfaced likely root causes, ranked issues by urgency and business impact, and recommended the most suitable resolution or escalation path.
AI acted as an investigation partner for support teams, continuously reviewing new and historical cases, narrowing possible causes, retrieving similar incidents, and proposing the next best action.
Reliability Design & Risk Exposure
Recommendations were supported by visible evidence from previous tickets, customer records, system events, and related incidents. Confidence levels and escalation criteria were introduced to prevent uncertain or sensitive cases from being resolved automatically. Resolution playbooks defined where the system could assist, where supervisor approval was required, and where specialist intervention was mandatory. Incorrect diagnosis could prolong incidents, repeat failures, or damage important customer relationships.
Integration Model
For Process: The platform was integrated into ticket intake, investigation, prioritization, escalation, and resolution workflows. For People: It reduced repetitive investigation work for agents while giving supervisors and specialist teams clearer visibility into complex or high-impact cases. For Data: It connected customer conversations, support records, account information, product usage, and operational events, while identifying missing or conflicting context before generating a recommendation.