RAG System for Live Customer Support
90%+ Intent Prediction with Sub-Second Responses Across Multiple Organizations
The Challenge
A leading customer communication platform needed to scale intelligent support across multiple client organizations. Support agents were overwhelmed by growing query volumes, and existing systems lacked the ability to understand customer intent in real time. The company needed a solution that could serve personalized, context-aware responses at sub-second latency while working reliably across diverse client knowledge bases.
Our Approach
We architected a multi-tenant RAG system capable of serving multiple organizations from a shared infrastructure. Each client's knowledge base was ingested, chunked, and indexed separately, with organization-level isolation for retrieval and response generation.
Intent classification was built as a high-accuracy prediction layer, achieving over 90% success rate. The model was trained on historical support interactions and continuously refined through A/B experiments that compared different retrieval strategies and prompt configurations.
Data-collection frameworks were designed from scratch to capture interaction patterns, feedback signals, and resolution outcomes. This data fed both model improvement cycles and comprehensive dashboards for monitoring AI performance.
The entire pipeline — from data preprocessing through model serving — was deployed in production with real-time monitoring, enabling the support teams and clients to benefit from highly personalized, sub-second responses.
The Results
Before this system, support agents manually handled every query across all client organizations — a process that couldn't scale. The platform replaced that bottleneck with an AI layer that routes and responds in real time, letting the company onboard new client organizations without proportionally growing its support team.
90%+ intent accuracy
<0.5s response time
A/B tested
Technologies Used
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