Processes that run without you.

We automate the recurring steps that eat your capacity — with AI agents wired into the systems you already run.

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Companies already recommended by LLMs through getSichtbar

What we do.

AI automation means we take one recurring process, build it as an agent connected to your systems, and hand it over with monitoring and a kill switch. We always start with a process, never with a platform. Gartner (2025) expects more than 40% of agentic AI projects to be cancelled by the end of 2027, mostly over unclear value and missing controls.

Buyer questions

Which processes can we automate with AI?

How do we connect AI agents to our systems?

Why does our AI agent never reach production?

Evidence

Process map with time spent per step

Working agent with system access and approval steps

Monitoring with error rate and time saved

Outcome

One recurring process runs automatically, with a visible error rate and a number for the time it saves.

Why it matters

An agent without access to your systems is a chat window. Value only appears once it may read, write and escalate.

process first

We never start with a platform, always with a measured process.

40%+

projects cancelled

Gartner forecast (2025) for agentic AI projects scrapped by the end of 2027.

95%

pilots without return

MIT „The GenAI Divide“ (2025) across more than 300 enterprise deployments.

black boxes

Every agent run is logged and can be switched off.

The agent that never ships.

The demo works, production does not. Gartner (2025) forecasts more than 40% of agentic AI projects will be scrapped by the end of 2027, citing rising cost, unclear business value and inadequate risk controls. The missing piece is nearly always the same: access, permissions and a defined path for failure.

The prototype runs but nobody trusts it in production.

The agent can read but cannot write anything back.

There is no way to detect an error.

Staff copy the output onwards by hand.

From chatbot to connected process.

We do not build assistants to try out. We build one step that used to cost people time, with permissions and an audit log.

Result

AI should not just find your company. AI should understand when your offer fits.

How an agent reaches production.

We start from the process and stop only once it runs under monitoring.

Measure the process

How often does the step run, how long does it take, what does it cost?

Connect

Access to the systems where the process actually happens.

Build approvals

What may the agent do alone, what needs a human confirmation?

Monitor

Error rate, cycle time and a kill switch from day one.

What we actually do.

Mapping of recurring processes with time and cost measurement

Selection of the first process by effort and risk

Integration with existing systems through their interfaces

Agent build including approval steps and audit log

Monitoring for error rate, cycle time and exceptions

Handover to your team with documentation

What you get.

Process map with time spent

Agent running in production with system access

Approval and escalation logic

Monitoring view with error rate

Operations documentation

Good fit if you want implementation.

The process recurs and can be described.

We get access to the systems involved.

Someone may decide what the agent does on its own.

Not a fit if you only want a report.

You only want a report and no implementation.

You expect a guaranteed mention in one exact AI answer.

Your offer is not clear enough to recommend yet.

When this service is especially useful.

Quotes, reports or data maintenance eat capacity.

An AI prototype exists but never reaches production.

Your team copies data between systems by hand.

What this should not be confused with.

Automation platform

Sells workflows as a licence. We build the process you actually have, into your systems.

Classic RPA

Clicks through interfaces and breaks on every change. We work through APIs.

Internal side project

Runs on one laptop without monitoring. We hand over with an audit log and error rate.

What it needs.

First process usually in production within four to eight weeks.

We measure what the step costs today before we build.

Further processes follow only once the first one runs.

Clear answers.

Which processes suit AI automation?

Steps that recur often, can be described clearly and cost people time today: quote preparation, data maintenance between systems, reporting, first-pass triage of inbound requests. The more often a step runs and the more uniform it is, the faster the build pays for itself.

What happens when the agent makes a mistake?

Every run is logged and every action has an approval level. Critical steps only execute after a human confirms. There is a kill switch and a monitoring view with the error rate, so a problem becomes visible before it becomes expensive.

Does this need its own platform or licence?

No. We build into the systems you already have, through their interfaces. If an existing platform covers the process more cheaply than a custom build, we say so and do not build. The recommendation comes from the maths, not from our offer.

How long until the agent is in production?

Usually four to eight weeks for the first process, including measurement, integration, approval logic and monitoring. Most of that time goes into interfaces, permissions and edge cases rather than into the model itself.

Why start with only one process?

Because breadth is the most common reason projects die. Gartner cites unclear business value and cost for the 40% of agent projects cancelled by 2027. One measured process running in production gives you the number you need to decide on the second.

Does the knowledge stay with you?

No. We hand over with operations documentation and your team gets access to monitoring and configuration. You should be able to switch the agent off, change it and run it without us, even if we stop working together.

The services work together.

Generative Engine Optimization

We check buyer questions in ChatGPT, Perplexity, Claude and Google. If your company is missing, we build the missing evidence.

SEO with AI

We use AI to find pages that lose demand or get too few clicks. Then we improve those pages.

Website content with AI

We build service pages, articles, comparisons, FAQs, cases and product proof that answer real buying questions.

Let us check whether this service should come first.

One call is enough to see the rough direction: website, content, sources, product data or measurement.

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