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 where you decide: on your SABER server, on your premises, or in a sovereign or standard cloud. The cost is fixed, and when your data changes the AI re-adapts — no starting from scratch.