Retrieval-Augmented Generation grounds AI answers in your actual product catalogue, brand guidelines, SOPs, and customer knowledge base. The result is AI that's accurate, citable, and on-brand — not a language model guessing from training data.
Retrieval-Augmented Generation (RAG) is a technique that gives a language model access to your specific documents — product catalogues, SOPs, return policies, FAQs — before generating a response. Without RAG, ChatGPT answers from generic training data and frequently hallucinates specifics about your brand. With RAG, the AI retrieves your actual content and generates answers grounded in what you've documented. This is the difference between an AI that guesses your return window and one that cites your exact 30-day policy.
We ingest: Shopify product descriptions and metafields, PDFs (supplement facts, certificates, user manuals), website pages, Notion and Confluence wikis, Google Docs, customer support ticket archives (anonymised), FAQ content, and CSV/JSON product data exports. We build custom connectors for each source with incremental sync — so your RAG system stays current as your product catalogue and documentation evolves.
For structured knowledge questions (return policy, product ingredients, shipping timelines, store compatibility), well-built RAG systems achieve 85–95% factual accuracy — comparable to a well-trained human agent. Accuracy depends heavily on the quality of your source documents and the RAG architecture design. Poor chunking, inadequate metadata, or embedding model mismatch are the most common accuracy killers, which is why we invest heavily in retrieval accuracy benchmarking before deployment.
For most DTC brands, we recommend starting with Pinecone (managed, no infrastructure overhead) or pgvector on PostgreSQL (if you're already on Postgres and want to minimise new services). For brands with complex filtering needs — retrieving content by product category, brand, or language — Weaviate's multi-tenancy and filter-then-search architecture often outperforms pure cosine similarity approaches. Database choice is always driven by your query patterns, scale requirements, and team's operational capacity.
A focused RAG system for a single use case (e.g., customer-facing product Q&A widget pulling from your Shopify and FAQ content) takes 3–6 weeks to design, build, evaluate, and deploy. A comprehensive internal knowledge base covering multiple document types, user roles, and interfaces takes 8–14 weeks. We run evaluation milestones every 2 weeks so you can validate retrieval accuracy before we invest in the interface layer.
Stop letting AI hallucinate about your brand. Our RAG systems ground every AI answer in your actual product data, policies, and brand knowledge.