We automate and transform your business
with artificial intelligence.

We work across the whole Data & AI cycle: AI agents, generative AI, automation, Data Engineering, predictive models, Computer Vision, BI, MLOps, audit and training. Each solution is designed around your business uses and integrated into your existing environment.

We start by identifying processes that are costly, repetitive, complex or heavily dependent on data. Each opportunity can then be assessed on its business value, its feasibility, its cost, its risks and its potential return on investment.

No. Whether a project makes sense depends more on the use case, the volumes, the processes and the potential value than on the size of the company. We adapt the architecture and the scope to each organisation's context.

Not necessarily. Some projects require a substantial history of data, whereas solutions based on LLMs, pre-trained models or agents can get started with much less. We assess the prerequisites right from the scoping stage.

Yes. We can integrate solutions with your CRM, ERP, line-of-business software, databases, APIs and existing applications so that AI fits directly into your processes rather than working as an isolated tool.

Yes. Where technical or economic uncertainty exists, a POC or a prototype makes it possible to test the use case on representative data, measure the performance obtained and decide objectively whether or not to pursue industrialisation.

It depends on the scope, the data, the integrations required and the level of industrialisation expected. A project can range from a targeted prototype to a complete platform deployed across several business processes; the timeline is therefore set after scoping.

The budget depends mainly on the complexity of the use case, the integrations, the data available, the level of security and the volume of users or of processing. We build an estimate suited to the actual scope rather than a standardised offer.

Security is built in from the architecture stage: access rights, isolation of environments, logging, encryption, control of the data sent to the models and the choice of hosting. Private architectures, or ones using open source models, can also be considered where that is necessary.

We favour observable indicators: time saved, volume processed, fewer errors, the accuracy of a model, lower costs, higher productivity or commercial impact. The aim is to be able to compare the results obtained with the objectives set at the outset.