AI solutions for business

We work on chatbots and virtual assistants, AI agents that perform tasks in your systems, process automation, generative AI grounded in company knowledge, advisory work and predictive models. Each solves a different problem, so we start by asking what you want to improve, not which technology to pick. The result is working software built into your processes, not just a demo.

First we choose one use case and check whether your data and processes can support it. Then we build a prototype on real examples, test it with users, and only then extend it to a production version, working iteratively. Timing depends on data quality and the number of integrations. A prototype lets you judge the effect early, before committing to a full rollout.

The biggest factors are data quality and availability, the number of systems to integrate, the required accuracy of answers, how the model is hosted, and the running costs of using models. We begin with a short analysis of the use case and split larger scopes into stages. That way you can start with a pilot and decide on next steps from its results, not from promises.

Language models do sometimes get things wrong, so we design to limit that risk: answers are grounded in your documents, we define what the system may do alone and what needs human approval, and we test on real cases. Data questions, including what is sent to external model providers, are settled before we start. We apply security management aligned with ISO/IEC 27001.

Not always. A chatbot or assistant based on documents mainly needs organized materials such as policies, manuals or price lists. Predictive models, on the other hand, need historical data in sufficient quantity and quality. If it is missing, we say so plainly and suggest organising or collecting data first. It is better to begin with a smaller, realistic use than to build something with nothing to learn from.