Retrieval-Augmented Generation

RAG, engineered for production, not demos

A single RAG type fails at enterprise scale. We architect the right mix.

Retrieval-Augmented Generation grounds a language model in your approved data so answers stay accurate and traceable. At enterprise scale, one naive vector search is not enough: products, documents, customers and partner systems each need a different retrieval strategy. We design, build and run the right combination, EU-hosted, with your code and no lock-in.

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Why one RAG type is not enough

Retrieval-Augmented Generation (RAG) connects a language model to your own data so it answers from approved sources instead of guessing. A demo runs one vector search over a folder of text. A production system at enterprise scale layers specialized retrieval per data type, fuses keyword and vector search, walks knowledge graphs for relationships, and routes each query to the right strategy. We engineer that full taxonomy, grounded, permission-aware and audited by default.

Grounded answers with citations, not hallucinations
Permission-aware retrieval, scoped before the model sees it
Hybrid, graph and agentic retrieval, not a single vector search
EU-hosted, your code, model-neutral, no lock-in

How RAG works, end to end

From your data to a grounded answer: ingested and embedded, retrieved through hybrid and graph search, reranked, then generated and checked.

Hybrid retrieval fuses vector, keyword and graph search. A guardrail, evaluation and audit layer wraps every step before the answer returns.

The RAG taxonomy

Specialized RAG, matched to the problem

Modern AI architectures use a taxonomy of retrieval patterns. We engineer across all three families and combine them per data asset.

Core architectural and algorithmic

The retrieval backbones, chosen by data shape and how exact the match must be.

Naive / Standard RAG

Single-pass vector search over text chunks.

Simple FAQ matching

GraphRAG

Knowledge graphs link entities, for example a customer to purchased products via a transaction edge.

Relationships and entities

Hybrid RAG

Fuses keyword search (BM25) with vector similarity.

Exact SKU and code matching

Hierarchical RAG (RAPTOR)

Recursively summarizes text into parent-child trees.

Long contracts and manuals

Multimodal RAG

Retrieves across text, images, video and audio at once.

Photos to product listings

Agentic and dynamic loops

Loops that decide, check and route, for multi-source answers and quality control.

Agentic RAG

Tool-equipped agents plan multi-step retrieval across separate data silos.

Cross-system answers

Corrective RAG (CRAG)

An evaluator judges retrieval quality and falls back to another source when it is poor.

Accuracy guarantees

Self-RAG

The model critiques its own output and retrieves again on demand.

Real-time quality control

Adaptive RAG

A router reads the query first, then sends it to a cheap or a heavy path.

Cost and latency control

Context and input engineering

Engineering each chunk and turn to carry the right context before it is embedded.

Conversational RAG

Factors in full dialogue history so follow-ups keep their referent.

Multi-turn assistants

Contextual Retrieval

Prepends global document context to each chunk before embedding.

No broken semantic links

HyDE

Generates a hypothetical answer first, then searches with it to bridge vocabulary gaps.

Slang to internal terms

GraphRAG: answers that follow relationships

Vector search alone matches text that looks similar. GraphRAG adds a layer of entities and typed relationships, so the system can answer questions that depend on how your data connects: a customer to their orders, an order to its products, a product to its policy. For commerce, support and engineering data, that is the difference between a plausible guess and a correct, traceable answer.

A knowledge graph links entities by typed edges, so a query can traverse from a customer to the exact products and documents that ground the answer.

Map RAG to your data assets

To scale, each entity type maps to the retrieval architecture that fits it. This is how we structure retrieval for an enterprise commerce platform.

	Optimal RAG strategy	Why this choice matters
E-commerce products	Hybrid RAG + GraphRAG	Stops semantic search from hallucinating inventory counts or matching the wrong size or SKU.
Documents	Hierarchical RAG + Conversational	Summarizes full manuals and contracts accurately while keeping the dialogue thread.
Customers	GraphRAG + context-aware	Finds structural links across order history to surface personalized buying paths.
Agencies and partners	Agentic RAG	Actively fetches data from third-party platforms through live, audited tool use.

Examples shown for a commerce platform; the same mapping method applies to manufacturing, finance and public-sector data.

Production target state

Structured Agentic RAG

In production you do not pick one RAG type. An Adaptive RAG router sits on top and sends each query down the right path.

The router classifies each query, then sends it to a simple lookup, a graph traversal or live agentic tools, reconverging through guardrails and evaluation.

What is my shipping status?

Agentic RAGqueries the logistics API live.

Any jackets that match the shoes I bought last month?

GraphRAGevaluates the customer-to-product purchase graph.

What each team gets

RAG decisions look different from each seat. Here is what matters to the people who sign off the architecture.

CTOs and IT leaders

You need answers grounded in your systems, not a chatbot that invents policy.

A named retrieval architecture, runs in your cloud, GDPR-grade, with evals on every change.

Enterprise and procurement

A buying committee has to audit how the system reaches an answer.

Citations, permission-aware retrieval and an audit log, with AVV and TOM readiness.

Startup CTOs and founders

You want working retrieval in weeks, not a research project.

A pragmatic Hybrid or Agentic RAG shipped in a 90-day pilot, then scaled.

Agencies and partners

Your client needs senior RAG work delivered under your brand.

White-label retrieval engineering, the same discipline behind our open-source work.

Public engineering you can inspect

Running on this site

Live

The assistant on this site is an agentic, tool-using system we built and run in production, not a demo behind a login.

Vendure Data Hub

Open source

A Vendure commerce plugin we built and published, public on GitHub. Two of our eleven engineered bundles are public.

View on GitHub

Pimcore Asset Pilot

Open source

A Pimcore asset bundle we built and published, public on GitHub and inspectable end to end.

View on GitHub

When RAG is not the answer

Pure real-time numeric truth, for example live inventory counts, belongs in a direct query, not retrieval.
Changing the model's tone or core behavior is a fine-tuning job, not a RAG job.
A tiny static knowledge base may be cheaper to put directly in the prompt.
Tasks with no source of truth to ground against are not a retrieval problem.

Questions teams ask about RAG

They fall into three families: core architectural patterns (Naive, GraphRAG, Hybrid, Hierarchical or RAPTOR, Multimodal), agentic and dynamic loops (Agentic RAG, Corrective or CRAG, Self-RAG, Adaptive RAG), and context engineering (Conversational, Contextual Retrieval, HyDE). Production systems combine several.

Agentic RAG gives the system tool-equipped agents that plan multi-step retrieval across separate data sources, including live APIs, instead of a single vector lookup. It is the right pattern when an answer needs data from more than one system.

Vector RAG is best for fuzzy text similarity. GraphRAG is best when the answer depends on relationships between entities, for example linking a customer to products through purchase history. Most enterprise systems use both behind a router.

RAG grounds the model in current, approved data and keeps answers traceable. Fine-tuning changes the model's tone, style or domain logic. They solve different problems and often run together; we advise which fits each case.

Products use Hybrid RAG plus GraphRAG so exact SKUs and inventory are not hallucinated; documents use Hierarchical RAG; customers use GraphRAG; partner systems use Agentic RAG. An Adaptive router ties them together.

Retrieval is the baseline. Production also needs evaluation, guardrails, permission-aware context, observability and routing. We engineer the full system, EU-hosted, with your code and no vendor lock-in.