On-Premise LLM Deployment
A private AI assistant for your team, running on open-source models on your own servers. Prompts, files and answers stay inside your network.
What you get
Model selection
We test a few open-weight models on your own tasks and hardware and pick the one that fits.
Installation
The model server is installed on your hardware or private cloud, with GPU sizing done before you buy.
Web chat
A chat interface for your team, built on an established open-source web UI, with history and file upload.
Internal API
An OpenAI-compatible endpoint, so your internal tools and scripts can use the same model.
Single sign-on
Sign-in through your existing directory, such as Active Directory, LDAP or SAML.
Training
Sessions for your team on good prompts and on what the assistant should and should not be used for.
From first call to daily use
Discovery
We agree on users, tasks, data rules and hardware, and choose two or three candidate models.
Pilot
One team uses the assistant on real tasks, and we compare the models on their own material.
Rollout
Access for everyone who needs it, monitoring, and logging as your policy requires.
Support
Model updates, new use cases and help for your administrators.
Models we deploy
Open-weight models from the main research labs. The choice depends on your tasks, languages and the hardware you have or plan to buy.
Why teams run their own model
No data leaves
Nothing is sent to a public AI provider, so sensitive files can be used in prompts.
Your languages
Models are chosen and tested for the languages your team actually works in.
Your own rules
You decide who can use it, what is logged and how long history is kept.
Other services
They work on the same infrastructure.
Who uses it
Teams that cannot send their documents to a public AI.
Frequently asked questions
Have a question that is not here?
What should a private-assistant pilot test?
Use the tasks staff perform today, with examples of acceptable answers and the languages they need. Compare candidate models on that material before deciding what to roll out.
What should we check before connecting an internal application?
Check whether the application can call an OpenAI-compatible API and which data it sends. The private model exposes an internal endpoint; include a representative request in the pilot.
Can we use hardware we already have?
Bring its specifications to discovery. We assess model size and capacity against your tasks, languages and expected number of users before recommending additional hardware.
What should we agree about access and conversation history?
Decide which staff groups need access, how they sign in, what is logged and how long history is kept. Those policies should be part of the rollout plan.
How do we choose a model for Arabic or mixed-language work?
Include your own Arabic and mixed-language examples in the pilot. Compare the answers on your actual tasks before selecting a model; model-family names alone do not establish how well it will handle your material.
Plan your private assistant
Tell us about your team, data and hardware, and we will propose a pilot.