Practical AI solutions, designed to solve your real business challenges.

A custom solution is worthwhile when your data, business rules or confidentiality requirements do not match standard tools, or when AI needs to be tightly integrated into your product. If an existing tool already does the job, we will tell you: custom development only makes sense when it adds clear extra value.

RAG, or retrieval-augmented generation, pairs a search engine over your documents with a language model. The model answers based on the passages it retrieves and can cite its sources. It is the right approach for querying large or frequently updated document bases without retraining a model every time the content changes.

It depends on the problem. Approaches built on pre-trained language or vision models often need only a few examples. A specific predictive model, on the other hand, requires a sufficient and representative history. During scoping, we review the data you have and tell you what is feasible before any development commitment.

We always start with the simplest option. An existing model, guided by good instructions and by your data through RAG, is often enough. Adapting or training a model becomes worthwhile for highly specialised tasks, unusual formats, or performance and cost requirements that general-purpose models cannot meet.

Yes. When confidentiality requires it, we deploy open-source models on your servers or in a private cloud of your choice, with no data sent to an external provider. We walk you through the trade-offs in performance, hardware requirements and running costs compared with APIs from the major providers.

From the scoping stage, we set metrics suited to the problem, such as precision, recall or forecast error, and measure them on test data the model has never seen. Results are compared with your current method, then tracked in production to detect any decline in performance over time.

Once the proof of concept is validated, we industrialise the solution: a secure API, integration into your applications, a user interface, performance monitoring, logging and a process for updating the model. We document everything and train your teams so the solution remains usable and maintainable over the long term.