Challenge
One of Europe’s largest retailers faced a growing operational bottleneck: fragmented exception handling that drained engineering resources and slowed incident response.
Monitoring tools were disconnected, forcing manual log inspection averaging 44 minutes per incident. Recurring errors were handled as new issues each time – no pattern memory, no institutional knowledge. Developers spent more time firefighting than building.
Solution
⋮IWConnect designed and built ResQAI, an AI-enhanced exception handling system that understands errors as they occur. Combining large language models (LLaMA2/LLaMA3) with Qdrant vector similarity search, the system detects recurring patterns, contextualizes issues against historical data, and proposes resolutions in real time.
A custom Python pipeline – built to replace third-party tools and eliminate licensing constraints – manages the full lifecycle from ingestion to automated Jira ticket creation. A unified Central Monitor App gives operations teams real-time visibility across all processors.
Business Value
The results were immediate: 37% reduction in mean time to resolution and 62% less manual triage workload. Ticket creation dropped from 44 minutes to under 2 minutes.
The system classifies recurring issues with 85% accuracy, enabling teams to address root causes instead of repeatedly treating symptoms. Operations staff reclaimed 15-20 hours weekly – time that shifted from reactive troubleshooting back to product development.
Ready to see how AI-enhanced operations could transform your exception handling? Download the full case study.
