ShopEase: A Generative AI-Based Multi-Agent Framework for Intelligent Enterprise Customer Support Using Hybrid Retrieval-Augmented Generation

2026-09-12

AI Summary

该论文提出ShopEase框架，利用生成式AI和多代理解决企业客户支持问题，通过混合检索增强生成方法提高回答准确性。

Abstract

Enterprise customer support systems must answer customer questions correctly, retrieve the right policy information, use customer context, and pass difficult cases to human agents when needed. This paper presents ShopEase, a Generative AI-based multi-agent framework for enterprise customer support. The system combines six components: Intent, CRM, Memory, Hybrid RAG, Escalation, and Supervisor, and uses LLaMA 3.2 running locally through Ollama for response generation. The retrieval module combines FAISS (dense retrieval) and BM25 (sparse retrieval), and six configurations are evaluated: BM25-only, FAISS-only, Fair RRF, Weighted RRF, RRF with Cross-Encoder, and Top-10 Hybrid with Cross-Encoder. Instead of using a fixed mapping between intent and policy, the policy category is decided directly from the retrieved documents. The system was evaluated on 2632 held-out customer queries across six categories: Refund, Return, Shipping, Cancellation, Damaged Product, and Unknown. FAISS-only achieved the highest accuracy of 85.37\% (2247 correct predictions), closely followed by Weighted RRF at 85.07\%. BM25-only achieved only 55.74\% accuracy. Adding cross-encoder reranking did not improve results: RRF with Cross-Encoder reached 83.24\%, and Top-10 Hybrid with Cross-Encoder reached 81.88\%, while also increasing response latency. Category-level analysis shows strong performance on Shipping, Cancellation, and Return, while Unknown queries remain the main source of errors. Statistical testing using McNemar's test shows no significant difference between FAISS-only and Weighted RRF, though both perform significantly better than Fair RRF and the cross-encoder configurations. Overall, dense retrieval gives the best accuracy on this dataset, and additional reranking adds processing time without improving classification performance.

Problem

• Enterprise Customer Support
• Generative AI
• Multi-Agent Framework
• Retrieval-Augmented Generation
• Accuracy

Innovation

• Generative AI
• Multi-Agent Framework
• Hybrid Retrieval-Augmented Generation
• FAISS
• BM25