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Business assistants, document search, contract analysis, data extraction, report generation, customer support, email processing, copilots, Voice AI or multimodal generation are among the possible use cases. The architecture depends on the need and on the data involved.

A custom assistant can be connected to your data, your applications and your business rules. It therefore does more than hold a conversation: it can search, summarise, contextualise and, where relevant, trigger actions in your information system.

RAG lets a model search for information in your own documents or databases before generating its answer. It is particularly suited to internal assistants, knowledge bases, contract research and applications that require answers grounded in your own sources.

RAG supplies the model with up-to-date information at the time of the query. Fine-tuning changes its behaviour on the basis of training examples. Depending on the need, we can use one, the other, or combine several approaches.

We can combine RAG, controlled sources, structured prompts, answer rules, automated evaluations, confidence thresholds and human validation. For sensitive uses, the aim is not to trust the model blindly but to frame precisely what it can produce.

Yes, depending on the architecture chosen. The options notably include professional services with suitable data policies, private cloud environments or open source models hosted on dedicated infrastructure.

There is no universally better model. We compare quality, cost, latency, languages, context, multimodal capabilities, confidentiality and hosting constraints in order to select the architecture that genuinely matches the project.

Yes. For significant use cases, it makes sense to evaluate several models on a set of representative scenarios. The decision can then be based on concrete measurements rather than on a provider's general reputation.

They depend mainly on the number of features, the data to be connected, the integrations, the volumes and the level of industrialisation. A targeted document search and an AI platform integrated into several processes are very different projects.

We notably track volumes, tokens, models used, caching strategies and RAG architecture. It is also possible to use different models depending on the complexity of the requests, in order to avoid systematically using the most expensive model.