AI-Powered First-Line Support Agent (RAG)

Overview

The project involved developing 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. The solution was designed to solve scalability challenges by automating repetitive, low-complexity questions that previously overwhelmed the human support team.

Achievements

The system achieved a 60–80% reduction in routine ticket volume and provided a positive ROI within 90 days of implementation. It enabled 24/7 support availability with sub-second response times, significantly improving customer satisfaction while maintaining 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.

Technologies Used