Local AI that responds with your knowledge

The pattern is the same; Here the runtime and data live in the on-premise infrastructure of this hub. We coordinate both catalogs so as not to duplicate projects.

In on-prem design, the goal is no. We define exceptions only if the customer agrees to a documented hybrid.

Every LLM can be hallucinating. We mitigate with RAG, citations, evaluation, and confidence thresholds — we don't promise zero error.

It depends on storage, memory, and chunking quality. We dimension in the diagnosis.

Only if specific tools are authorized, with minimum permissions and, if applicable, human approval.

Yes: internal assistant on internal policies, procedures and FAQs is a typical case.

Inference does need to compute; the index can live on disk/CPU. We size it together.

We define re-ingestion pipeline (manual or scheduled) and corpus ownership.

It can be API, internal chat, or integration into existing tools. It is agreed on scope.

RAG/agent pipeline, initial indexed corpus, evaluation battery and operation documentation.