We design vertical AI systems for companies in any sector, across any business process: from GDPR-compliant local models, to sizing the machines for inference, through to integration with the internal systems you already use. No company data ever leaves for the cloud.
Public cloud solutions are not always compatible with industrial confidentiality requirements, NDA contracts or the GDPR. We design the alternative: models that run inside your own infrastructure.
Technical drawings, confidential documents and know-how stay on your network. No upload to third-party APIs.
Architectures built from the start to meet privacy by design, data minimisation and traceability requirements.
A one-off investment in hardware and design, with no cloud fees that grow with usage or number of users.
No dependency on external connectivity or third-party cloud availability: inference runs locally, always.
A complete path: model selection, hardware sizing and connection to the systems you already use every day.
Selection of open-weight models suited to the use case, with fine-tuning or RAG on company documents for relevant, verifiable answers.
Choice and configuration of hardware (GPU/CPU, RAM, storage) based on the real load, with model optimisation and quantisation.
Connection to ERP, PLM/CAD, document management, email and ticketing via APIs, plugins or dedicated automations, where technically possible.
We apply AI to your company's real processes, in any department: documentation search, automatic reporting, customer support, back office.
We train internal teams to use the implemented AI tools day to day, so adoption is real and not just technical.
Model updates, performance monitoring and technical assistance after release into production.
The inference server talks to the systems already in use in the company, without any data crossing external networks.
A structured path, with compliance and performance checks at every stage.
Analysis of processes, the data involved and the existing internal systems. Review of GDPR compliance constraints.
Model choice, sizing of the hardware for inference and design of the integration points.
On-premise installation, fine-tuning or RAG on company documents, connection to internal systems.
Staff training and ongoing assistance with system updates and performance.
Let's talk: we analyse your processes and propose a tailored architecture. I reply within one working day.
Book a consultation →No. The model and the inference run on hardware installed in your network: documents, drawings and conversations are never sent to external cloud services.
It depends on the model and the expected load: from a single workstation with a dedicated GPU up to an on-premise server for multiple users. Sizing is part of the audit phase.
Where the system exposes an API or a documented interface, yes. We assess the technical feasibility of the integration case by case during the initial audit.
For sensitive data or high volumes, yes: you pay for the hardware once, with no fees that grow with users or tokens consumed, and the data stays yours.