Building Enterprise RAG on Amazon Bedrock and OpenSearch: A Production Blueprint

Proof-of-concept AI chatbots built on simple wrapper scripts fail when deployed in enterprise production. They hallucinate, breach company security policies, leak customer records across tenants, and struggle with multi-gigabyte document corpora.

Enterprise Retrieval-Augmented Generation (RAG) requires a secure, governed cloud architecture where foundation models interact with private vector stores strictly inside your cloud security perimeter. Here is how 3 Dices Technology architects RAG on AWS.

Foundation models via Amazon Bedrock

Amazon Bedrock provides access to state-of-the-art LLMs (Anthropic Claude, Meta Llama, Amazon Titan) through a unified serverless API without sending company data outside your AWS boundary. No customer prompts or proprietary documents are used to train the base models, which keeps Bedrock aligned with strict enterprise confidentiality standards.

High-performance vector storage with OpenSearch Serverless

A robust RAG system depends on semantic search quality. We implement Amazon OpenSearch Serverless with the k-NN (k-nearest neighbors) plugin:

• Intelligent chunking: splitting documents into contextual paragraphs with overlap to preserve conceptual continuity.
• Embeddings pipeline: transforming text into high-dimensional vectors via Amazon Titan Embeddings v2.
• Hybrid search: combining semantic vector similarity with BM25 keyword matching to accurately retrieve technical part numbers, acronyms, and product codes.

Multi-tenant security and role-based access control

In SaaS applications, Tenant A must never access Tenant B's vectors. We enforce tenant isolation at both the metadata query filter level and the AWS IAM policy layer, giving complete cryptographic and logical separation across customer datasets.

The production RAG stack

The components of our enterprise AI deployment pipeline:

• Ingestion and parsing: AWS Lambda plus Textract, extracting text, tables, and metadata from documents.
• Embedding generation: Amazon Titan Multimodal, generating 1024-dimension semantic vector embeddings.
• Vector indexing: OpenSearch Serverless, a k-NN index with hybrid keyword and vector search.
• Orchestration layer: Python LangChain or the Bedrock API, handling context assembly, prompt formatting, and guardrails.
• LLM inference: Anthropic Claude on Bedrock, producing grounded answers with direct citation sources.

Where to start

Ready to embed enterprise-grade AI into your application? Our work on GenAI on AWS and AI agents builds custom document extractors and secure RAG systems that stay inside your AWS boundary.