From your data to your AI.

1 · The knowledge graph

We ingest your documents, history and procedures and structure them into a knowledge graph: your domain, made explicit and traceable. It stays available for exact retrieval and lets us see exactly where an answer comes from.

2 · RAG — retrieval

Retrieval-augmented generation lets the model look up the exact passage in your data before answering, with the source cited. This grounds answers in your reality from day one — no waiting for training, and no invented facts.

3 · Adaptation — adapters

We then adapt the model itself: lightweight adapters fine-tuned on your data so the model absorbs your vocabulary, style and rules. It learns your field without rewriting a whole foundation model — fast and updateable.

Why both

RAG gives exactness and provenance; adaptation gives fluency in your domain. Together they beat either one alone: the model both knows how you work and can cite where each answer comes from.

Deployed on your side

The result runs on your SABER server, on your premises. Nothing leaves for the cloud, the cost is fixed, and when your data changes the AI re-adapts — no starting from scratch.

RAG or fine-tuning? When to use each →