Enterprise AI that runs inside your systems and rules

The difference from consumer AI tools lies less in the model than in everything around it. An enterprise system has to know who is asking and what they may see, connect to business systems with their own access rules, leave a record that security and audit can follow, and keep working when a model version changes. A large part of the engineering effort goes there.

We build these systems with your engineers, in your environment, one use case at a time:

• Enterprise assistants that answer from your documents and systems, with citations and the reader's own permissions
• AI agents that handle routine steps of business cases and pass decisions to people
• AI features in the products you sell, with the serving and infrastructure underneath
• MCP servers that connect approved internal APIs to the AI tools your staff already use
• Private AI: open-weight models served on your infrastructure or in an EU account you control
• Coding agents rolled out across engineering, with shared configuration, cost control and training

Some data may not leave infrastructure you control. In that case we serve open-weight models on your own servers or in a cloud account and region you choose, sized for the tasks they have to do. Where a commercial model is acceptable, we document the data flow, retention and region so your data protection officer can decide.

Running models yourself adds operating work: capacity, upgrades, monitoring and on-call cover. We make that cost visible before you choose. A mix can work well, with sensitive or high-volume tasks on a private model and the rest through an approved provider.

We report usage by team and task, not by individual employee. Legal and compliance decisions stay with your advisers; we prepare the engineering evidence they need.

• Access checked in the systems behind each tool, not only in the prompt
• Audit logs of tool calls and decisions, without stored credentials
• Approval steps for any action that changes data or sends it out
• Evaluation sets that run before every model, prompt or tool change
• Data flow documentation for your data protection officer, and logging and reporting rules agreed with your works council

A manufacturer wants its service technicians to get answers from manuals, past tickets and the parts catalogue while on site. The documents include confidential designs, so the assistant runs on an open-weight model in the company's own data centre. Answers cite the manual page or ticket they come from, and technicians only get answers from documents their role may open.

Before launch the assistant is tested on real technician questions, including ones the documents cannot answer, and the platform team receives the runbook for adding sources and replacing the model.

We start with one use case and a measured baseline, build with your engineers in your environment, and widen access only after the test results and early production data justify it. Your platform team owns the result: code in your repositories, tests in your CI, monitoring in your tools.