AI agents for business that take over the work. Not someday. Now.

Yes. For contractors, installers and other construction companies we build AI agents for site administration and daily reports, planning and purchasing, specification and tender analysis and the Wkb file, connected to the planning and project administration you already use. There are no construction cases on our site yet. The principle behind a dictated daily report is already running at In het Veld, where conversations are automatically turned into reports and action points. The approach per application is on our page about AI in construction.

A chatbot answers questions in a conversation. An AI agent carries out tasks: it reads emails and documents, updates your CRM, prepares a quote or starts a process, within the permissions you give it. A chatbot can be the front end of an agent, but the work sits in the connections to your systems.

RPA follows fixed rules and breaks the moment the input deviates. An AI agent thinks along, weighs context, and can handle natural language and unfamiliar situations. RPA is good for stable, repetitive tasks. An AI agent is good for tasks where someone would normally still have to keep an eye on things.

Yes. We connect agents to the tools you already use: Moneybird, AFAS, Exact, your CRM, Google Workspace, Outlook, WhatsApp. We build on what is already there, not alongside it. No standalone tool your team has to learn separately all over again.

Look for a partner that connects agents to your own systems instead of selling a standalone tool, can show agents running in production at real clients, agrees a fixed price up front, builds in logging and human oversight for critical actions, and stays involved after launch. An agent is never finished on day one: it gets better through feedback from real use.

An agent works best as part of how your business runs: with access to the right data, clear agreements on what it may decide by itself and a fixed place in the process. That is why we also look at the process, the integrations and the data layer around it. That way one agent grows into a new way of working, instead of an experiment that gets left on the shelf.

A first working agent is up in 2 to 4 weeks. After that we keep improving it based on what we see in production. No months of writing specs first, but something tangible fast that grows with your business.

It depends on three things: how many systems the agent has to drive, how critical the task is (and so how much control and logging it needs), and whether it is delivered once or improved continuously. A simple agent that takes over one task is a smaller project than an agent that drives multiple systems; those usually run on a retainer model. We always agree a fixed price up front. Book a call for a concrete estimate for your use case.

For critical actions we build in a human-in-the-loop. The agent proposes, a person approves. For routine tasks we let the agent work on its own with monitoring and logging as a safety net. You can always see what happens and you can correct it.

Through feedback and monitoring. We build in logging so you can see what the agent does on every action, and the agent learns from corrections. Better every week, not one big release every two years. That is the difference from traditional software.