Document database & RAG: Using AI to query your documents

RAG (Retrieval-Augmented Generation) is an artificial intelligence method that enables a language model to generate answers from a pre-existing document base, such as reports, FAQs or internal archives.

Language models alone are not always enough: they may lack precision, timeliness or traceability. RAG overcomes these limitations by connecting AI to a dynamic document baseguaranteeing answers reliable, contextualized and verifiable.

Vector indexing : to transform your data into rapidly searchable semantic databases, and find the right information at the right time.
Embeddings : to represent the meaning of content and reconcile similar information, whatever its form (text, image, relational data).
Complete RAG pipeline : retrieval, reranking, prompt injection and generation orchestrated to maximize relevance and reduce approximations.
Context optimization : intelligent chunking, memory window management and document coverage strategies to enrich responses.
Performance evaluation : monitoring of groundedness, hallucination rate and relevance scores to guarantee quality and reliability.

Objective: combine the power of language models with the solidity of business data, to provide precise, traceable answers to strategic challenges.