AI-Powered First-Line Support Agent (RAG)

Overview

Developed an intelligent automation system to handle Tier-1 customer inquiries for a microsite building platform. By leveraging advanced Retrieval-Augmented Generation (RAG), the system analyzes a knowledge base of over 280 articles to provide instant and accurate responses. Designed to solve scalability challenges by automating repetitive, low-complexity questions that previously overwhelmed the human support team.

Achievements

• Achieved a 60–80% reduction in routine ticket volume • Provided a positive ROI within 90 days of implementation • Enabled 24/7 support availability with sub-second response times • Maintained high-fidelity accuracy through automated hallucination detection

Responsibilities

• Designed a sophisticated state-based graph architecture to route queries, grade document relevance, and manage multi-step support workflows
• Implemented a hybrid semantic search layer featuring query expansion and cross-encoder reranking to ensure precise information retrieval
• Developed an automated 'watchdog' infrastructure for incremental indexing, allowing the system to self-update whenever documentation changes
• Integrated built-in validation mechanisms and source attribution to ensure all AI responses are grounded in official documentation
• Containerized the application for seamless cloud deployment and built an internal monitoring dashboard for real-time performance tracking

Technologies Used