RAG Architecture in Burgos — AI That Knows Your Business, Not the Internet

Imagine a very intelligent assistant that, before answering any question, first searches your company's document library and reads the relevant pages. That is exactly what a RAG system does — automatically, in milliseconds. The AI does not invent; it responds based on what it has found in your documents.

When a user asks a question, the system converts it into a mathematical representation (embedding), searches the vector database for the most semantically similar document fragments, passes those fragments to the AI model as context, and the model generates a response grounded in that specific content.

Fine-tuning modifies the AI model itself by training it on new data — expensive, slow and difficult to update. RAG keeps the model unchanged and provides it with dynamic context at query time. This means you can update your knowledge base without retraining the model, and the system always reflects the latest version of your documents.

OpenAI's standard API terms allow using submitted data to improve their models unless you explicitly opt out (available only on enterprise plans). Even with opt-out, the data transits through US infrastructure, which creates GDPR complications for EU businesses handling personal data or trade secrets.

With a private RAG architecture, the AI model processes your documents within your own infrastructure. Nothing is sent to external servers. You get the analytical power of modern AI with complete control over where your data lives and who can access it.

We specialise in deploying RAG systems that meet EU data protection requirements — either on your own servers or in EU-region private cloud infrastructure.

We audit your document repository, define the ingestion strategy (chunking size, overlap, metadata tagging) and build the pipeline that converts your documents into searchable vector embeddings.

We select and configure the vector database best suited to your scale and privacy requirements.

We integrate the RAG retrieval layer with your chosen AI model: fully local open-source models (Llama 3, Mistral, Qwen) via Ollama or vLLM for maximum privacy, or private API access to Claude or GPT-4 on enterprise plans with data processing agreements.

The entire stack — document store, vector database, embedding service, LLM and API layer — is deployed on your own VPS, dedicated server or private VPC. You own the infrastructure and can audit every component.

Engineers, technicians or compliance officers can ask natural language questions and get precise answers with exact references to the relevant sections of manuals or regulatory documents.

A RAG-powered chatbot that answers based on your actual product documentation, warranty terms and support guides — not generic AI responses. Fewer hallucinations, more accurate answers, higher customer trust.

Employees can ask HR policy questions, search contracts or find the relevant clause in an agreement — without waiting for HR to respond or manually searching hundreds of documents.

Sales teams can query technical specifications, compatibility information and pricing directly from the company's product database — even during a call with a customer.

We catalogue your documents, define scope, identify data sensitivity levels and design the knowledge base architecture. Deliverable: implementation plan with timeline and infrastructure requirements.

We build the document processing pipeline, configure the vector database and run the initial ingestion. We test retrieval quality with a representative set of queries before proceeding.

We integrate the AI model, engineer the system prompt and context injection strategy, and tune the response format for your use case. We evaluate accuracy and hallucination rate against your test query set.

We deploy to production infrastructure, run load and accuracy testing, and hand over with complete documentation, admin guide and team training session.