Architecting a Low-Latency RAG System for Fast-Moving Consumer Goods (FMCG) Customer Support: A Case Study in Industrial Software Deployment

Abstrak

Deploying large language models (LLMs) in industrial customer support environments require balancing response accuracy with system latency. This study presents the software architecture and implementation of a Retrieval-Augmented Generation (RAG) system designed for the Fast-Moving Consumer Goods (FMCG) sector. Addressing the limitations of generic LLMs in domain-specific knowledge tasks, we engineered a retrieval-augmented inference pipeline that integrates unstructured data ingestion, Pinecone for vector indexing, and Groq-based inference for low-latency response generation. The proposed system aims to improve response grounding by incorporating organizational product information into the generation process while maintaining responsive interaction times suitable for customer support applications. This study details the software architecture, system integration approach, and experimental evaluation of the proposed deployment-oriented RAG framework in an industrial FMCG case-study setting.

Identifier

• OpenAlex: https://openalex.org/W7167092418
• DOI: https://doi.org/10.14569/ijacsa.2026.0170679