Alexander Fürst
fuerst.one
Alexander Fuerstcreative technologycogent interfaceseffective automation
UX+AI Engineer
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01 / AI automation & prototypes
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UX + engineering, together

Less copying between tools. More moving things forward.

Your team shouldn’t have to be the integration between its tools. I build automations that connect the work — and prototypes that turn “could this work?” into something you can actually try.

Let’s talk about your workflow↗See the work ↓

Start with one repetitive task or one product question. No technical specification needed.

With Alexander Fuerst · Würzburg, Germany

● ● ●Conversion Y - AI powered Surveys for B2B↗
Product screenshot: Conversion Y - AI powered Surveys for B2B
Selected workConversion Y - AI powered Surveys for B2B

02 / What you get

Start with the work. Build what helps.

The useful question is where your team loses time or your idea needs evidence. That determines what to automate, where AI fits and what needs a person’s judgment.

01

Connect the tools you already use

Move information between systems, remove repeated data entry and make handoffs explicit. Start with one workflow and the access your tools actually provide.

Workflow map · API integrations · error and retry handling

02

Give AI a defined job

Extract, organize or summarize information where a language model can help. Define review points and test against real examples, so uncertain output has somewhere to go.

AI workflow · evaluation examples · human review points

03

Put the idea in someone’s hands

Test the riskiest assumption with a working prototype. Give users something to react to and your team a concrete basis for deciding what to build next.

Focused scope · working prototype · findings and next steps

A clear first step

AI workflow assessment

Map one recurring workflow, compare AI and conventional automation, and define a useful first implementation step.

See the scope & deliverables ↗

A focused, paid engagement. Scope and fee agreed before work begins.

03 / Selected work

Built, not just proposed.

AI-supported survey analysis and a personal multi-agent development tool. Concrete examples of turning information and repeated steps into usable software.

01Startup project

Conversion Y - AI powered Surveys for B2B ↗

A B2B survey product that combines survey creation, website integration and automatic analysis, helping businesses turn responses into actionable information.

Client / project context
Conversion Y
Product screenshot: Conversion Y - AI powered Surveys for B2B

Reported project outcomes

Total survey creation and analysis time reduced by >50%

Results reported in this project’s portfolio record. Case-specific outcomes, not a forecast for your project.

  • React.js
  • Next.js
  • Node
  • Tailwind
  • PostgreSQL
  • Zustand
Explore the case study ↗
02Personal project

build-manager ↗

A personal development tool coordinating AI agents through a staged build process, with a web interface for live status, steering and review. A self-directed project, not a client commission.

Client / project context
Fuerst.one
Product screenshot: build-manager

Reported project outcomes

12 agents running spec generation, code and review simultaneously

Results reported in this project’s portfolio record. Case-specific outcomes, not a forecast for your project.

  • React.js
  • Node
  • Tailwind
  • Hono
  • SQLite
  • Headless Chrome
Explore the case study ↗

04 / How we work

From problem to working product

  1. 01

    Pick a useful starting point

    We trace one workflow or isolate one product question. We look at the inputs, the desired result and the constraints before choosing tools.

  2. 02

    Build a small, real version

    I connect the critical systems or implement the core interaction. We try real examples and make limits, failure cases and review steps visible.

  3. 03

    Decide what deserves to scale

    We compare the result with the starting point. If it is useful, we plan monitoring, documentation and the next step. A prototype’s remaining gaps stay explicit.

A good place to start

For teams with repeated manual handoffs, information that needs processing, or an idea that needs a working test before a larger investment.

05 / Practical questions

Before we talk

Does every automation need AI?+

No. Rules, APIs and ordinary code are often the clearer solution. I use AI when the task benefits from interpreting language or handling variable input, with checks appropriate to the consequences.

Can you work with our existing tools and sensitive data?+

We first check API access, permissions, hosting and what information may leave your systems. That determines the approach. Don’t send confidential data in your first email; we can agree a suitable way to review it.

Will a prototype be ready for production?+

A prototype is built to answer a question. Production may also need access controls, monitoring, resilience and a support plan. We’ll make those gaps clear and agree any production work separately.

How do we start, and what will it cost?+

Describe one repeated task or the idea you want to test, plus the tools involved. We can define a first milestone and discuss the effort. Scope, access and operational needs determine the cost; there’s no one-size-fits-all package.

06 / Your next step

What are you tired of doing by hand?

Or which idea needs to become real enough to test? Tell me what happens today and what you’d like to happen instead.

Let’s talk about your workflow↗
Or email directlyalexander@fuerst.one

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Also working onComplex product interfaces ↗