Plugged into your stack.
Confined to your network.

A RAG chatbot uses retrieval-augmented generation: it retrieves relevant passages from your knowledge base before generating an answer, grounding responses in your own verified documents.

By grounding every answer in retrieved source documents and citing them, RAG limits the model to your verified content rather than free-form generation, sharply reducing hallucinated answers.

You run the embedding model, vector store, and LLM serving runtime inside your infrastructure, with no calls to external APIs. Cressu packages this entire stack for on-prem or air-gapped deployment.

Cressu applies confidence thresholds and predefined rules before any action, and logs every step of retrieval, reasoning, and execution so supervisors can review and refine outcomes.

Cressu integrates with ticketing systems, CRMs, internal databases, and wikis via private APIs and custom connectors, keeping all data flow inside your network perimeter.