AI Agents & digital staff
More than a chatbot — digital staff that actually work.
We build AI agents that take on real workflows in your business — research, sort, prepare, reply — connected to your tools and in the EU, with every service provider disclosed. No rented tool: the agent runs on your own accounts and belongs to you. »Digital staff« is an image, not a product name: what is meant is a program that carries out one bounded task repeatedly and traceably — with access to exactly the tools it needs for that, and with a log showing what it did.
What we deliver
- Agents for your specific tasks
- Connected to your tools & data
- in the EU, with every service provider disclosed, no lock-in
- You keep control, data & model choice
- A log of every step instead of a black box
- Fixed limits: what the agent may do, what stays with people
What you get out of it
Why it matters
Recurring tasks tie up exactly the people who should really be doing more important things — research, data upkeep, standard replies. An agent that reliably takes over those workflows gives you that time back, without handing processes to an outside platform or having to add headcount. The reason smaller firms lag here is not the budget but that nobody in-house can take responsibility for something like this — the figures in the evidence below say exactly that. That is the role we fill: we decide with you which task is suitable at all, and hand the agent over to you documented, instead of selling a subscription.
Numbers and sources
26 % of enterprises in Germany used AI in 2025 — among firms with 10 to 49 employees, 23 %.
Among enterprises with 250 or more employees it is 57 %. The gap is the real finding: large companies have whole departments for this, smaller ones rarely do. Among enterprises that considered AI and then did not adopt it, lack of knowledge tops the list of reasons at 72 %, followed by uncertainty about legal consequences (62 %) and data protection concerns (60 %); only 32 % name excessive cost. So the obstacle is not the price — which is exactly where we start, instead of selling tools nobody ends up operating.
Source: Federal Statistical Office (Destatis), ICT survey of enterprises 2025, table on enterprises using artificial intelligence technologies by employee size class: destatis.de/DE/Themen/Branchen-Unternehmen/Unternehmen/IKT-in-Unternehmen-IKT-Branche/Tabellen/ikti-unternehmen-kuenstliche-intelligenz.html. The obstacles come from the neighbouring table on reasons against using AI technologies, as of 24 November 2025. Corroborated by Eurostat isoc_eb_ai (25,97 %).
How we work
Understand workflows, not sell a tool
We sit down with you and look at which tasks cost time every day and are clear enough to describe that an agent can reliably take them on. From that we define what the digital colleague should actually do — and what deliberately stays with a human.
Build and connect
We build the agent to fit and connect it to the tools and data sources you already use — such as inbox, calendar, documents or your CRM. The whole thing runs on EU infrastructure that you keep access to.
Test, hand over, support
Before the agent goes live, we test it on real cases and adjust it. After that we hand it over to you documented and stay reachable for changes when your workflows shift.
Set limits and approvals
Before a line of code exists we clarify which steps the agent may carry out itself and which need approval. Anything with an outward effect or a financial consequence belongs in the second group — that is not caution, it is the condition for being able to let it run at all.
Build in logging and checks
Every tool call, every source read and every write operation is recorded. Without that log you can neither pin down an error nor show that a process ran correctly.
Keep the model exchangeable
The task, the connections and the rules belong to you; the language model is one part behind them. If price, legal situation or quality change, we swap the model and leave the rest standing — without the agent having to be rebuilt.
Tools we use for this
You get a digital colleague that genuinely belongs to you and takes on routine tasks in a way that noticeably frees up your team's time for the real work.
Common questions
Does an AI agent replace my staff?
No. It takes on clearly bounded, recurring tasks — researching, sorting, preparing, standard replies. Decisions and everything that needs judgement stay with your team, which is thereby freed up for the more demanding work.
What happens to my data?
The agent runs in the EU, with every service provider disclosed, and you keep access to the data and the choice of model. We only connect the data sources needed for the task at hand — there's no vendor lock-in that your data is tied to.
How do I know whether it's worth it for my workflows?
We clarify that in the first conversation. An agent pays off where a task comes up regularly and can be clearly described. Where that isn't the case, we tell you honestly, instead of building something that doesn't hold up day to day.
What is an AI agent — and how does it differ from a chatbot?
A chatbot answers. An agent acts: it calls tools, reads and writes in systems, waits for results and picks the next step from what it finds. In practice: a chatbot explains how an invoice gets filed; an agent files it, names it to your scheme and enters the line in the overview. The difference is not the language ability but the access — which is why the important question is not what it can do but what it is allowed to do.
Which tasks suit digital assistants — and which do not?
Suitable is anything that comes up regularly, can be described clearly and has a checkable output: sorting incoming items, pulling data out of documents, assembling research, preparing standard replies, reconciling master data. Unsuitable is anything where the right path depends on the individual case and a mistake gets expensive — price commitments, terminations, medical or legal advice, any outward effect without approval. As a rule of thumb: what you could not explain to a new hire on one page is not ready yet.
How do I know what the agent actually did?
From the log. Every call, every source read and every write operation is recorded, so you can follow step by step how a result came about. This is not a nice-to-have: without that log you cannot pin down an error and, in a dispute, cannot show that a process ran correctly either. Anyone offering you an agent without an inspectable log is selling you a black box.
What happens when the agent gets something wrong?
It will get things wrong — the only question is where that shows up. So the limits are set before the build: which systems it reaches, which steps need approval, how much data it may touch, and when it stops instead of guessing. If a check fails, it halts and reports rather than carrying on. An agent without such limits is not a faster colleague but a faster mistake.
Does the agent stay usable if I want to change the model?
Yes — which is why we separate workflow and model from the start. The task, the connections and the rules sit with you; the language model is an exchangeable part behind them. If price, legal situation or quality change, we swap it and leave the rest standing. What you need to keep for that are the accounts and the description of the workflow — both belong to you, not to us.