AI implementation that holds up in everyday work
From use case to running solution: we build AI assistants, automations and integrations with your existing tools — and train your team so the solution is actually used day to day.
AI automation means letting AI-powered systems handle recurring tasks — reliably, transparently and embedded in your existing workflows.
What we implement
A good AI idea is quickly stated. The difference is decided in the implementation: a solution that works in a test but isn't used in daily life is wasted time. That's why we think about implementation from the workplace outward and don't build an isolated silo, but integrate AI where your team already works. The result is tools that take over routine, reduce errors and measurably save time — and leave the decision where it belongs: with people.
AI assistants & chatbots
Assistants for customer service and internal knowledge transfer that answer from your own content — not out of nowhere.
Process automation
Routine tasks like document creation, classification or data upkeep run automatically instead of by hand.
API & tool integration
Connection to CRM, ERP, ticketing systems, knowledge bases and Microsoft 365 via stable, maintainable interfaces.
Data pipelines
Data is reliably merged, prepared and made available — the foundation for any robust AI solution.
Our implementation process.
Start small, prove the value, scale safely — in four steps.
Concept
We pick a use case with clear, measurable value and define scope, data paths and success criteria.
Pilot
You get a working version early and give feedback from practice before we roll it out widely.
Integration
We harden the solution, connect it to your systems and bring it stably into production.
Training & operation
Your team is trained; on request we handle maintenance and expansion to further processes.
Technology & data protection
We are technology-open and vendor-neutral: which model and which platform are used depends on your use case, your systems and your data-protection requirements — not on a partnership with a vendor. For sensitive data we rely on European or self-hosted models, clear data flows and proper data processing instead of data sprawl. That keeps AI automation transparent, maintainable and GDPR-compliant — even as the tool landscape keeps evolving.
Example results
What does AI automation look like in practice? A service assistant answers inquiries around the clock based on your own documents and cleanly hands complex cases over to staff. A proposal automation creates a first draft from a few key facts that a human only reviews and approves. A document workflow reads incoming invoices or forms, classifies them and passes the data to accounting. What they have in common: they take over routine but leave the decision with people — and exactly this balance is the difference between a gimmick and a real tool.
What matters to us is the measurable effect: AI automation has to be judged by numbers — time saved, faster response times, fewer errors or higher completion rates. That's why we define before the start what success should look like for a use case and keep an eye on those metrics in operation. So the solution doesn't become a prestige project but a tool whose value you can see in black and white — and which forms the basis for deciding which further processes benefit next.
Implementation ideally begins with AI consulting that determines the most worthwhile use case. Where AI connects to your website or an application, AI implementation works closely with our web development. How we realize complex digital platforms is shown by cases like Callpoint and localsearch in our projects.
Frequently asked questions
What does AI automation cost?
It depends on the project and on the use case, the number of integrations and your data situation. Our advice: start small and with measurable value, then scale only after proven success. After a free initial conversation you receive a concrete proposal with a fixed scope — we deliberately do not quote flat rates.
How long until the pilot?
Depending on complexity, we deliver a first productive pilot in four to eight weeks. We work iteratively: you see a working version early and can course-correct based on real experience instead of waiting months for a finished system.
Can it be integrated with our existing tools?
Usually yes. We integrate AI into existing systems such as CRM, ERP, ticketing systems, knowledge bases or Microsoft 365 — via APIs and established interfaces. The goal is a solution that works where your team already is, rather than yet another isolated silo.
Who runs the solution afterwards?
That is your decision. On request we take over operation, maintenance and further development; equally, we can hand the solution over so your team runs it itself. The necessary training is part of the delivery in either case.
Does our data stay with us?
Data protection is part of every implementation. Where it makes sense, we rely on European or self-hosted models, sign data-processing agreements and ensure that no sensitive data flows into third-party systems unintentionally. Which data is processed is transparent and handled in a GDPR-compliant way.
Questions, projects, help?
Get in touch — we’ll reply within one business day.
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