Retrieval-Augmented Generation: AI that cites instead of inventing.
RAG is the technique that connects an AI model with your real knowledge base. Assistants and chatbots that respond with authentic data from your company, not generic answers. Citable, auditable, updatable.
Four steps separating good RAG from bad RAG.
- 01
Knowledge base indexing
We process your documents (PDFs, manuals, contracts, FAQ, intranet) and chunk them. We generate vector embeddings with a model suited to your language and domain.
- 02
Vector storage
Embeddings go to a vector database (Pinecone, Weaviate, Qdrant or pgvector). Indexed for instant semantic search.
- 03
Retrieval + generation
When a query arrives: we vectorize it, retrieve the most relevant chunks from your base, pass them to the AI model with the question, which generates an answer based ONLY on that information.
- 04
Citations and traceability
The answer includes which documents were consulted. If the user doubts, they can check the source. If the knowledge base is updated, the answer updates the next day.
Technologies we master.
- OpenAI Embeddings
- Anthropic Claude
- LangChain
- LlamaIndex
- Pinecone
- Weaviate
- Qdrant
- PostgreSQL pgvector
- Sentence Transformers
- Cohere Rerank
About Retrieval-Augmented Generation.
01 What does Retrieval-Augmented Generation mean?
Instead of the AI generating answers only with its general knowledge (training), it first RETRIEVES information from YOUR knowledge base and then GENERATES the answer from there.
02 Why not just use ChatGPT?
ChatGPT does not know your internal documents, contracts or manuals. RAG fixes that by connecting it to your real information.
Does your team search the same info every week?
A quick call to evaluate whether RAG is the answer.