Retrieval-Augmented Generation (RAG) is transforming how businesses leverage their internal knowledge bases, but most reference architectures assume enterprise-scale resources. This article presents a grounded, SME-friendly RAG architecture that balances cost, performance, and maintainability. It walks through document preprocessing, chunking strategies, embedding model selection, vector database choices (e.g., Milvus, Qdrant), and retrieval optimization techniques like hybrid search and re-ranking. The author also discusses deployment considerations, including API gateway design, caching, and monitoring. For technical founders and engineering leaders evaluating RAG for their product, this architecture provides a concrete starting point that avoids over-engineering while remaining production-viable. The focus on modularity and incremental scaling makes it especially relevant for startups and mid-sized teams.
A practical guide to constructing an enterprise-level RAG architecture tailored for SMEs, covering ingestion, retrieval, and deployment.