Intelligent semantic search: exploit your data with embeddings and knowledge graphs

Semantic search is a technology that enables AI to understand the meaning and intent behind a query, rather than just the exact keywords. It uses natural language processing (NLP) and learning models to provide more relevant and contextual results.

An organization's data is often scattered, heterogeneous and difficult to exploit. Thanks to semantic search, embeddings and knowledge graphs, it is possible to give them a new meaning. new dimensiontransforming raw information into structured, interconnected and actionable knowledge.

Vector representation (Embeddings) : to go beyond keyword research and gain an understanding of meaning, improve recommendations and strengthen RAG pipelines.
Knowledge graphs : to reveal hidden relationships between entities, structure data according to business ontologies and make connections visible and exploitable.
Hybrid search and semantics : to combine the power of vectors and the logic of graphs, offering contextualized, explainable and traceable answers.
Modular, scalable approach : to integrate these solutions into complex environments, while guaranteeing transparency, explicability and alignment with strategic objectives.

Objective: transform fragmented data into a network of intelligent knowledge, capable of accelerating access to information, improving the relevance of decisions and creating genuine strategic leverage.