RAG systems
RAG: make your AI answer with your company data
Retrieval-Augmented Generation (RAG) indexes your documents and forces the model to cite real passages. It fits policies, contracts, catalogs and SOPs. From Medellín we ship it with Pinecone, pgvector or Weaviate, in Spanish, with access control. A measurable pilot typically costs USD 3,000–6,000 in 4–8 weeks.
What you get
Citations, not inventions
Each answer points to the document and section used.
Your data stays yours
Index on your cloud or ours under NDA. We do not train third-party models on your corpus.
Better than a generic chatbot
A general model does not know your rates or internal policy. RAG does.
Support in Spanish
Chunking and embeddings designed for Colombian documents (contracts, DIAN, labor).
How we work
1. Corpus
Which documents, permissions and freshness.
2. Indexing
Chunking, embeddings and retrieval eval.
3. Q&A pilot
A set of real questions from your team.
4. Channel
Web, Slack, WhatsApp or intranet with logs.
Frequently asked questions
RAG or fine-tuning?
For knowledge that changes (policies, prices, contracts) RAG wins. Fine-tuning fits style or rigid format. We almost always start with RAG.
How much does RAG cost in Colombia?
Pilot on a scoped corpus: about USD 3,000–6,000. Large bases or several departments are quoted separately. Free diagnosis.
Does it work with scanned PDFs?
Yes, with OCR first. Scan quality sets answer quality.
Do you host it in Medellín or in the cloud?
Wherever you ask: AWS, Azure, GCP or on-prem. The implementation team is in Medellín.
Investment ranges also on the quote tool and contact.