What we implement
Modern AI, on your own hardware
Private assistants
A chat assistant for your teams that answers from your own documents, procedures, and records, under your access rules, in Arabic, French, and English.
Document search and RAG
Retrieval-augmented generation over contracts, files, reports, and archives, so staff can ask questions and get answers with the source passage shown.
Document intelligence
Reading scanned and handwritten documents, extracting fields, classifying, and summarizing, for administrations, hospitals, and back offices that run on paper.
Speech and meetings
Transcription of meetings, interviews, and dictation, and summaries of what was decided, with the audio never leaving your network.
Vision
Image and video models for inspection, counting, and monitoring, running beside the cameras that feed them.
Integration
AI features wired into the systems you already run, through interfaces on your own network.
The sovereign AI stack
An audit trail that starts and ends in your country
Most AI products today are thin interfaces over a foreign API. Every prompt, every document, every customer record travels abroad, and the organization that sent it cannot see what happens next. For many organizations in your country, that is not an option.
We deploy open-weight language, vision, and speech models on infrastructure you control, configure and tune them for your domain, and build tooling around them, such as llamanager, our software for running models on your own hardware and sharing them across a team. You get the capability of modern AI, and a system whose every data flow you can show to a regulator.
In practice
What a local AI deployment involves
- Models
- Open-weight models chosen for the task and for the languages your documents are written in, with versions recorded so every answer can be traced to the model that gave it.
- Hardware
- From a single workstation with a graphics card to a small cluster. We size it against your real workload and tell you what each option costs and delivers.
- Access
- Accounts, roles, and logs on your side. The assistant only reads what the person asking is allowed to read.
- Trade-offs
- Local AI is sometimes slower or costlier than a foreign cloud. We tell you where, and we show you what you gain in return.
How a deployment runs
From a first use case to daily use
Use cases
We find the two or three tasks where AI saves real time, and the data each one needs.
Pilot
One use case, on one server, with real documents and real users, measured before anything grows.
Integration
Connection to your document stores, your user directory, and your existing applications.
Rollout
Training for the teams, rules of use, and monitoring of quality and load.
Handover
Documentation, model updates on your schedule, and support, with nothing that reports back to us or anyone else.
Local AI
Bring AI in without letting data out
Tell us what your teams spend their time on and where your documents live. We will come back with a first use case and the hardware it needs.
Plan your local AI