Independent AI workflow diagnosis and optimization
Focused on real-world systems that need to work reliably
— not just look correct.
I help diagnose AI workflows that are close — but not reliable yet.
I work with businesses, freelancers, and teams using AI in real production workflows — especially when the setup looks promising on the surface, but the output is still inconsistent or unreliable.
That can show up in many ways: prompts that need fine-tuning, translations that miss context, automations that break between steps, terminology that drifts, or outputs that are good enough sometimes but not dependable enough to trust.
My approach is practical and systems-focused. I look at how the workflow is actually set up, where the weak point is, and what needs to change first.
Sometimes the issue is the prompt.
Sometimes it is the workflow logic.
Sometimes it is the handoff between tools — or the overall design of the system.
How I approach real issues
I work directly on real workflows — not demos or theory — across translation, automation, and multi-step AI systems.
Most AI issues are not obvious failures — they are small mismatches that accumulate across prompts, tools, and workflow steps.
Example — tone and naturalness
Original output
„Dein Laden ist ein Betrug“
Issue
Technically correct, but too literal and unnatural in context.
Improved version
„Dein Laden ist Abzocke“
What changed
The phrasing shifts from literal translation to natural spoken language, improving tone and making the output feel natural and credible.
Example — structure and flow
Original output
„Als ob du nicht denkst, dass das ein Problem ist.“
Issue
Overly literal structure, unnatural in real dialogue.
Improved version
„Als wär das hier kein Problem.“
What changed
More natural phrasing, better rhythm, and alignment with spoken German.
How I approach real issues
Background
Before focusing on AI workflow diagnosis, I built experience in language, translation, and content-driven workflows.
That background helps me spot the kinds of AI issues that often go unnoticed — problems in tone, structure, meaning, formatting, and context that make output look correct at first glance, but fail under closer use.
Over time, this work expanded into designing AI-assisted systems and structured workflows, including prompt-driven pipelines, multi-step decision logic, and controlled content generation.
This includes working with custom model setups and self-evaluating outputs to refine constraints, reduce drift, and ensure more consistent behavior in real-world use.
I have also worked with custom model setups and self-evaluated outputs to refine constraints, reduce drift, and better align generated results with specific requirements — particularly in workflows where consistency and downstream behavior matter.
Focus
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AI workflow diagnosis
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prompt and setup review
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translation and subtitle workflows
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output consistency and reliability
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tool handoffs and workflow weak points
What you can expect
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a clear, grounded review of your setup
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practical feedback in plain language
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attention to both workflow logic and output quality
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a focus on what to improve first
