SMEs & management
For companies that want to put AI to use and start pragmatically.
05AI Projects & Build
← Back to the offeringAI ideas become working solutions.
Many organizations want to use AI safely but have neither an implementation team of their own nor a clear path from idea to solution. I help to frame, sharpen and actually deliver AI initiatives: as a chatbot, assistant, agent, workflow, automation or prototype. Depending on the project, I deliver it myself or work together with technical partners.
For companies that don't just want to discuss AI, but bring it into practice responsibly and pragmatically.
Positioning
Pragmatic delivery, without becoming arbitrary.
Sometimes a quick prototype is enough. Sometimes a more robust solution is needed. What matters is that the project makes business, technical and organizational sense.
That includes questioning ideas critically: not every use case needs AI, not every automation is worth it, and not every demo should go into production.
That's why good implementation starts with the problem, not the tool.
Who it's for
For organizations that want to use AI but have no in-house builder.
Departments & teams
For areas with concrete ideas for chatbots, assistants or automations that shouldn't just be tried out, but actually built.
Innovation & digital leads
For internal innovation initiatives that want to test new AI-based products or services as a prototype or MVP.
What we can build
AI solutions you can actually use day to day.
Privacy-aware chatbots
Chatbots for internal or external use cases, for example for knowledge bases, policies, manuals, product information, FAQs, service processes or onboarding.
Internal AI assistants
Assistants that support employees with recurring tasks: research, summarizing, draft texts, analysis, document work, preparing decisions or compiling information.
AI agents
Agents that take on or prepare structured sequences of tasks: gathering information, producing interim results, suggesting next steps, checking content or supporting processes.
Automations and workflows
Workflows that reduce manual intermediate steps: sorting requests, preparing documents, extracting information, creating reports, triggering notifications or passing data between tools.
Prototypes and MVPs
First versions of new AI-based products, services or internal tools. Fast enough to learn from. Clean enough to build on.
Formats
Depending on how mature the idea is.
From a first consulting session to guided implementation, depending on how concrete the idea already is.
AI project check
For companies that want to know whether an AI idea makes sense and is feasible.
Result
- an assessment of the use case
- an initial technical and organizational evaluation
- risks and open questions
- a recommendation for the next step
Concept & build plan
For companies that want to plan a concrete AI solution.
Result
- a sharpened use case
- a requirements sketch
- a possible approach
- a tool and architecture proposal
- an effort estimate for a first build
Prototype, MVP or project delivery
For companies that need a first working version or a guided build over several weeks.
Result
- a testable prototype, MVP or internal solution
- ongoing coordination and technical implementation
- tests with realistic examples
- documented assumptions, limits and open points
- a recommendation for operation, further development or productive use
How it works
Pragmatic, but not arbitrary.
Clarify idea and goal
We start by asking which problem to solve. Which task should become easier, faster or better? Who will use it? What would a good result look like?
Check feasibility and risks
We check which data, tools, systems and conditions are relevant. This also includes data protection, confidentiality, quality assurance and possible limits of the solution.
Create the concept
The idea becomes a clear concept: functions, the user flow, data sources, technical implementation, review steps, roles and next expansion stages.
Build a first version
We implement a first working version. Depending on the project, it becomes an internal tool, a workflow, an assistant or a solid prototype.
Test and improve
The solution is tested with real or realistic examples. Error cases, limits and weaknesses in the user flow are surfaced and fixed.
Handover and next steps
At the end there's a usable solution or a solid prototype. We also clarify what's needed for operation, further development, internal approval or technical hardening.
Why with me
AI doesn't just need tools, it needs good product thinking.
I work with generative AI, automation and agentic workflows every day. At the same time I come from digital product development and have spent many years guiding projects at the interface between business requirements, users and technical implementation.
This role is especially important in AI projects. Many ideas sound simple at first but quickly become complex: business requirements, data, tools, quality assurance, internal approvals and technical implementation all have to fit together.
Depending on the project, I deliver it myself or work together with technical partners. That keeps the offering pragmatic, but not arbitrary: consulting, concept work, product understanding and technical delivery mesh together.
Gerald Aichholzer
Advisor, trainer & speaker on generative AI
Outcome
In the end it's clear what was built and what makes sense next.
A usable first version
A first solution that can be tested with realistic tasks and shows what holds up in business, technical and organizational terms.
Clarity on limits and risks
An assessment of quality, data protection, tooling, operation and the development still needed.
A basis for decisions
A solid, usable state to decide on internally: develop further, integrate, expand or deliberately stop.
Less AI fog
Not just an idea or a strategy buzzword, but a tangible next step.
Frequent questions
What often gets asked beforehand.
Is this consulting or implementation?
Both. Some projects start with consulting and end with a concept. Others go straight into prototyping or implementation. What matters is what the company needs and how concrete the initial idea already is.
Can you implement complete AI projects?
Yes, for many use cases we can guide you from the idea to a working result. For larger technical integrations or specialized enterprise systems, we work together with your internal IT or existing service providers.
Are the solutions data-protection compliant?
Data protection always has to be assessed case by case. We consider data protection, confidentiality, tool choice and governance from the start. Whether a solution is permissible in your company depends on data, hosting, contracts, internal requirements and the specific purpose.
Do we already need a concrete use case?
No. A rough idea is enough to start. As a first step, we clarify whether it should become a sensible use case, a project check or a concrete prototype.
How is this different from classic agencies?
The focus isn't only on technical implementation. We start with the problem and check the rationale, the data, the users, tooling and operations, then build. It's not about selling a tool, but about developing a fitting solution.
Before and after
Not every AI project starts with building.
02
AI Workshops & Training
For teams that want to build shared understanding, safe usage and practical AI skills before or during an AI project.
03Coding for Non-Coders
For people without a programming background who want to try out first ideas, mini-tools or prototypes themselves and prepare them better.
04AI Transformation & Sparring
For organizations that first want to sort out which use cases are sensible, feasible and responsible.
Inquiry
Let's turn your AI idea into a concrete project.
You have an AI idea, an automation topic or simply the wish to use AI sensibly and safely? Then a short message about your starting point is enough to begin.
I'll get back to you with a first assessment and a proposal for the next sensible step.