RAG Pipelines

Retrieval-Augmented Generation (RAG) solves the core problem of using language models on private enterprise data: a model trained on public data does not know your internal documentation, your product specifications, your regulatory filings, or your historical records. RAG retrieves relevant documents from your knowledge base at inference time and provides them as context to the model — so the model can answer questions about your data without being trained on it, and without that data leaving your infrastructure.

A production RAG pipeline is more than a vector database and an embedding model. The retrieval architecture must handle document chunking strategies that preserve context across chunk boundaries, embedding models appropriate for the domain and language of your content, hybrid search combining semantic and keyword retrieval for better coverage, re-ranking of retrieved documents to surface the most relevant content, and citation of sources in the generated output so users can verify claims. Each of these components requires engineering judgment informed by the specific retrieval task.

In regulated industries, RAG pipelines have a compliance dimension that general-purpose implementations ignore. The retrieval layer must enforce access controls — a user who is not authorized to access a document should not receive information derived from it, even indirectly through an AI response. For HIPAA-regulated content, the retrieval layer must enforce PHI access permissions. For financial services, it must prevent retrieval of information that would constitute material non-public information in certain contexts. These constraints are architectural, not instructional.

We design RAG architectures from the retrieval task outward — selecting chunking strategies, embedding models, and retrieval approaches based on the specific content and query patterns of each engagement. Access-controlled retrieval is a standard requirement for regulated industry deployments. We implement hybrid search, re-ranking, and source citation as baseline capabilities, not optional features.

Compliance-Native Architecture Guide

Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. Covers data architecture, access controls, audit trails, and vendor due diligence.