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arXiv · 2601.12024

Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews

Kartikey Singh Bhandari·Tanish Jain·Archit Agrawal·Dhruv Kumar·Praveen Kumar·Pratik Narang
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Abstract

Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommendations remains difficult. Standard sentiment/aspect analysis is largely descriptive, while direct prompting of large language models (LLMs) often yields generic and repetitive advice that is weakly grounded in user feedback. We propose a hierarchical decision-support pipeline that explicitly separates signal compression, problem abstraction, candidate generation, objective-based evaluation, and cost-aware routing into different agents. This architectural decomposition produces auditable intermediate artifacts and enables controllable trade-offs between advice quality and token budget. Experiments on Yelp reviews from three service domains show consistent improvements over single-pass LLM baselines across multiple advice quality dimensions, including actionability, relevance, and non-redundancy. A human evaluation further indicates that users generally prefer our system's recommendations. These results highlight the value of structured agentic decomposition for scalable, cost-aware business decision support.

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Kartikey Singh Bhandari, Tanish Jain, Archit Agrawal, Dhruv Kumar, Praveen Kumar, Pratik Narang. 2026-01-17. Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews. https://arxiv.org/abs/2601.12024

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