RAG Development

AI That Knows Your Business — Not Just the Internet.

Generic LLMs hallucinate about your products, misquote your policies, and know nothing about your specific customer base. Retrieval-Augmented Generation connects LLMs to your proprietary knowledge base so every AI response is grounded in accurate, up-to-date information about your actual DTC business.

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RAG Development Services

Ground Your AI in Accurate, Current DTC Knowledge

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RAG Architecture Design

End-to-end RAG system architecture — selecting vector databases (Pinecone, Weaviate, Chroma), embedding models, chunking strategies, and retrieval approaches for your knowledge base.

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Knowledge Base Engineering

Ingestion and processing of your DTC knowledge — product catalogues, brand guidelines, policies, FAQs — into searchable vector representations with optimal chunking and metadata.

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Semantic Search Implementation

Production-grade semantic search using dense retrieval, hybrid search, and re-ranking — ensuring the most relevant context is always retrieved for each LLM query.

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LLM Integration & Prompting

Integration of retrieval results into LLM prompts — context formatting, source attribution, confidence thresholds, and fallback handling for robust, grounded AI responses.

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RAG Evaluation Framework

Systematic evaluation of RAG quality — retrieval accuracy, answer faithfulness, factual correctness, and business metric correlation — with continuous improvement pipeline.

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Real-Time Knowledge Updates

Automated knowledge base update pipelines keeping your RAG system current as products, policies, and information change — no manual re-indexing required.

95%

Reduction in AI hallucinations with RAG vs base LLM

40%

Improvement in customer service AI resolution accuracy

Real-time

Knowledge base updates propagate within minutes

100%

Source attribution for every AI response through RAG

Frequently Asked Questions

Scale D2C's RAG Development service covers strategy, implementation, integration with your DTC tech stack, and ongoing optimisation. Our team has delivered RAG Development for DTC and ecommerce brands across beauty, health, fashion, and B2B — from Series A startups through to publicly listed companies.

RAG Development impacts DTC revenue by improving operational efficiency, customer experience, or marketing performance. Scale D2C defines clear, agreed KPIs — revenue uplift, cost reduction, or conversion improvement — before every RAG Development engagement, so success is never ambiguous.

Focused RAG Development implementations typically take 8–12 weeks. Projects with multiple integrations or data complexity run 16–24 weeks. Scale D2C provides a detailed project plan with milestone dates at the end of the discovery phase — no timeline surprises mid-project.

Scale D2C structures RAG Development content and pages with AEO and GEO best practices — FAQ schema, structured data, entity markup, and topical authority content — so your brand is cited in AI-generated answers on ChatGPT, Perplexity, Google Gemini, Claude, Deepseek, and Sarvam AI.

Scale D2C brings DTC commercial expertise and deep RAG Development technical capability together. Unlike generalist agencies, we understand how RAG Development fits into a DTC growth strategy — every decision is made with your revenue goals in mind, not just technical delivery metrics.

AI That Knows Your Business — Not Just the Internet.

Ground Your AI in Accurate, Current DTC Knowledge

Frequently Asked Questions

Build AI That Knows Your Products, Not Just the Internet