Smart Hub and RAG: turning your audits into a knowledge base

RAG (Retrieval-Augmented Generation) is an architecture that combines a search engine and a language model: before generating an answer, the system retrieves the most relevant passages from a document base, then builds its response from those excerpts, citing them. Unlike a model queried in isolation, it cannot answer beyond what its sources actually document.

Applied to a company audit, this means every answer, every document, every generated report becomes a source that a conversational assistant can query: "what is the company's DevOps maturity level?", "what security risks were identified?". The answer is explicitly grounded in the data collected, with its sources cited.

The value goes beyond simple lookup: an executive, an investor, or a consultant can explore an audit spanning several hundred questions in a few exchanges, without re-reading every report in full. It is also a safeguard against hallucination: if the information does not exist in the base, the assistant must say so explicitly rather than inventing a plausible-sounding answer.

An audit thus becomes a reusable asset over time, one that can be consulted months after it was produced, rather than a static deliverable frozen at the moment of its creation.