Under the hood, our AI stacks.

An LLM (Large Language Model) is an artificial intelligence system designed to understand and generate text in natural language. It is trained on huge volumes of textual data (books, articles, websites, dialogues) to learn language structures, word meanings, and their relationships. Based on deep neural networks, especially the transformer architecture, it predicts the most likely words in a given context.

RAG (Retrieval-Augmented Generation) is an AI approach that combines information retrieval and text generation. The model fetches relevant documents from a knowledge base, then uses them as context to produce more accurate, factual, and up-to-date answers, reducing hallucinations and improving system reliability.

A vector database, used in RAG, stores embeddings: numerical representations of text meaning. It quickly retrieves content that is semantically close to a query. This information is then provided to the AI model, which relies on it to generate more accurate, relevant, and contextualized answers.

A connector is a software or hardware component that enables interoperability between applications, services, or equipment. It translates, secures, and transports data between heterogeneous interfaces, manages protocols and formats, and facilitates integration, automated exchanges, reliable flows, and monitoring of digital communications.

An API (Application Programming Interface) is a set of rules that allows different software to communicate with each other. It defines how data is requested, sent, and received. APIs make it easier to integrate services, automate exchanges, and let developers use external functionality without knowing its internal implementation.