LLM-Powered Customer Support Systems for Multi-Tenant SaaS: Design and Evaluation

Vasanthi Jangala Naga

Abstract: Customer support automation is indispensable to the SaaS platforms as they scale up, gain complexity, and present greater operating costs. Traditional rule-based and intent-driven chatbots struggle to generalize across varied, unstructured user questions and require a lot of manual upkeep. Large language models open the door to advanced functionality in chatbots that reason contextually and understand the underlying natural language better but rolling them out reliably in enterprise SaaS settings raises problems associated with hallucinations, latency, cost, and keeping data isolated across tenants. This paper adopts a system-centric approach to LLM-powered customer support chatbots, optimized for multitenant SaaS environments. We present an RAG architecture that roots LLM responses in the company's knowledge base and introduces human-in-the-loop defenses that enhance reliability. We evaluate the proposed system on industry-standard customer support metrics using representative SaaS workloads. Results demonstrate clear improvements in first-contact resolution, answer accuracy, and average handling time relative to traditional automation techniques and highlight the viability of LLM-powered chatbots for enterprise SaaS deployments.

Keywords: Large Language Models, SaaS Observability, Customer Support Automation, Enterprise AI, Retrieval-Augmented Generation

How to Cite?: Vasanthi Jangala Naga, "LLM-Powered Customer Support Systems for Multi-Tenant SaaS: Design and Evaluation", Volume 14 Issue 6, June 2025, International Journal of Science and Research (IJSR), Pages: 1954-1959, https://www.ijsr.net/getabstract.php?paperid=SR250526235136, DOI: https://dx.doi.org/10.21275/SR250526235136