Objective

Our ambition is to enable any business user to ask their question directly to an AI and get a reliable answer - with no query to write and no waiting for IT.

For an AI agent to produce reliable answers, it must first understand the business domain it queries. This is exactly the role of the ontology: structuring this business knowledge to make it usable by the AI agent. However, this structure alone is not enough for the AI agent to reason correctly, as it does not know the business vocabulary and practices. This is where the augmented ontology comes in: it enriches this business knowledge with additional semantic layers

Technical and business impact

On the technical side, the augmented ontology becomes the foundation the AI agent relies on to reason, rather than trusting its probabilities alone. Combined with the 5 semantic layers, it guarantees consistent and traceable answers. It is the main lever for reliability and the absence of hallucinations. In addition, the AI agent reasons on the Knowledge Graph, that is, on real data, rather than on what it might generate by default. Each answer can thus be linked to a verifiable source in the graph.

On the business side, it means regained autonomy: creating an HR ontology, refining a description, adding synonyms or specifying a business rule - without writing a single line of code. The AI agent becomes more accurate with each iteration, driven by the business teams who know the field.

The augmented ontology is not an extra technical layer: it is the common language between your HR data, your teams and your AI - created in a few clicks, enriched endlessly.