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ShopEase framework uses LLaMA 3.2 for enterprise customer support

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces ShopEase, a multi-agent framework designed for intelligent enterprise customer support. This system integrates six components, including intent recognition, CRM interaction, memory management, a hybrid retrieval-augmented generation (RAG) module, escalation capabilities, and a supervisor. ShopEase utilizes LLaMA 3.2 for response generation, running locally via Ollama. The retrieval system was tested with various configurations, combining FAISS (dense retrieval) and BM25 (sparse retrieval), with FAISS-only achieving the highest accuracy of 85.37%. The research found that dense retrieval performed best, and adding cross-encoder reranking increased latency without improving classification accuracy. AI

IMPACT This framework could improve efficiency and accuracy in enterprise customer support by leveraging advanced retrieval and generation techniques.

RANK_REASON Research paper detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ShopEase framework uses LLaMA 3.2 for enterprise customer support

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Research paper detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, product, infra
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High
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aakash Kumar Tiwari, Somesh Kumar ·

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

    arXiv:2609.13856v1 Announce Type: new 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 Generat…

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