Enterprise AI Environment
When employees paste company data into public chatbots, nobody controls where it goes. We build an AI environment of your own: models, access rights, logs and rules, so teams can use AI on company data with control over what happens to it.
What is included
- Choosing where it runs: on-premise, private cloud, public cloud or hybrid
- Deploying and connecting language models (LLM)
- Access control, logging and usage limits
- Agent harness: the tools, rules and checks agents work within
- Quality evaluation and monitoring of AI answers
What you get
- One controlled place for AI work in the company
- Company data stays within the boundaries you set
- A record of who used AI, with which data
- A foundation for agents, RAG and integrations
Who it is for
Companies that want to use AI on internal data but cannot send it to public services, as well as teams whose separate AI experiments need to become one managed environment.
We work with companies worldwide.
Frequently asked questions
Can everything run on our own servers?
Yes, on-premise deployment is possible. If your requirements allow it, we can also deploy in a private cloud or on AWS, Azure or Google Cloud, or combine the options. We compare them at the start.
What is an agent harness?
The environment around an AI agent: which tools it can use, what data it can reach, which rules apply and how its work is checked. A good harness is what makes an agent reliable enough for real work.
Which models do you use?
We choose them for the task, your budget and where the data must stay. The environment is built so that a model can be replaced without rebuilding everything.
Do you use this approach yourselves?
Yes. Our own delivery team is made up of AI agents working in pairs, where one agent does the work and another reviews it.
On-premise or in the cloud
Everything we build can run where your data is allowed to be. We choose the option together, based on your security requirements, the law that applies to you and your budget.
On-premise
On your own servers, with data and models staying inside your perimeter.
Private cloud
A dedicated environment in the cloud, isolated from other customers.
Public cloud
AWS, Microsoft Azure or Google Cloud: managed services for a faster start and flexible scaling.
Hybrid
Sensitive data stays with you, while the rest of the workload runs in the cloud.
Free 30-minute data review
Tell us about your task. In a 30-minute online call we will find where your data breaks down and what to fix first. Free and with no obligation.