Automation and AI
Less manual data handling. More time for meaningful work.
I connect existing tools, APIs and AI models into a controlled workflow. Automation needs a measurable purpose, error handling and an audit trail — not only an impressive demo.
When it is worth talking
The solution starts with the right problem.
- →The same data is copied between email, CRM and spreadsheets.
- →The team repeatedly prepares similar reports or replies.
- →You need an assistant that uses company documents and knowledge.
- →You want to test AI without immediately building a large system.
Scope
What the engagement can include
- 01
process map and automation opportunity analysis
- 02
API integrations, webhooks and data flows
- 03
an assistant connected to a selected model and knowledge source
- 04
validation, error handling, logs and security limits
- 05
operating documentation and solution handover
Approach
From a decision to a working solution.
- 01
Process and goal
We measure what currently takes time and what the automation must deliver.
- 02
Pilot
I build a small workflow using real examples before expanding it.
- 03
Integration
I connect the tools and add secure data handling and error control.
- 04
Measurement
We evaluate time saved, output quality and the next opportunities.
FAQ
Questions before we begin.
Should every process be automated?+
No. I first consider frequency, error cost and data availability. A simple integration may be better than AI.
Can AI use company documents?+
Yes, after defining access rules, knowledge updates and confidential-data protection.
Can it run without supervision?+
It can run on a schedule, but it should have logs, alerts and clear cases that require a human decision.
Have an idea?
Tell me what is not working.
Let's define the next step.
Tell me briefly what is not working today or what you want to launch. I will reply with a practical next step — with no obligation.