AI Solutions in Practice

We work across industries — wherever processes, decisions and data play a central role. Our experience covers finance, insurance, logistics, energy, manufacturing, automotive, healthcare, pharma, consumer goods and defence.

Many of these industries face similar challenges: cost pressure, talent shortages, complex operations and the ambition to deploy AI responsibly and effectively. That is precisely where we help — with solutions that are commercially relevant and work in practice.

We deploy AI wherever workflows can be improved, decisions supported or resources used more efficiently. This includes sales, marketing, controlling, HR, support, production, planning, product development and IT.

We work along four key innovation fields:

1. AI on the product

AI enhances or improves existing products and enables new features, services or markets.

2. AI in core processes

We optimize value-creating operations such as production, logistics or operational decisions — more efficiently, more scalably and more sustainably.

3. AI in supporting processes

Finance, HR, customer support, procurement or administration benefit from greater automation, better data and more stable operations.

4. New business models with AI

We develop approaches where data and AI become the product — unlocking new revenue models or services.

We work with organizations of all kinds — from startups and small businesses to mid-market companies and large enterprises. What matters is not the size of the company or its AI maturity, but the commitment to deploy AI meaningfully and responsibly.

Our projects range from small, focused initiatives in growing businesses to large-scale transformation programmes and complex, highly regulated AI systems in critical infrastructure. In every case, we bring the right approach, the necessary technical depth and scalable solutions — so AI creates impact where it is needed.

The duration of an AI project depends heavily on how complex the underlying challenge is, how custom the solution needs to be, and whether established technologies and existing building blocks can be used. Clearly scoped problems can be implemented much faster than projects that reach deep into existing processes, data, and structures.

• When building on existing AI solutions — such as our AI platform Galilea — first AI projects can be realized within days.
• For custom-built AI systems, a first deployable AI pilot is typically possible within a few weeks, allowing use cases to be tested and evaluated early.
• A comprehensive, production-ready version of a custom AI system is often delivered within a clearly structured 3-month sprint — for example through our 100-Day MVP format.
• Larger and more complex AI systems with deep integration into existing processes, databases, interfaces, and infrastructure, as well as high requirements for scalability and operations, can take up to a year of development time.

A good AI project fits the company strategy and targets areas that directly affect competitiveness. It improves processes where time, money, or quality are currently being lost, and creates room for entrepreneurial action.

A good AI project delivers greater efficiency, better quality, and higher speed. It helps companies make decisions faster, work more reliably, and deploy scarce resources more precisely. It often frees employees from routine tasks and strengthens exactly the areas that are decisive for differentiation and growth.

Measurability and impact are key to a good AI project. This is what turns artificial intelligence into a strategic tool — rather than an experiment.

Entry-level AI projects work best when they are clearly scoped and show results quickly. Projects with the following characteristics tend to perform particularly well:

• Clear use case: A concrete problem with an unambiguous benefit, one that works without major IT dependencies and offers a sensible expansion path.
• Confirmed feasibility: Technology, data, and target vision are aligned. Open research questions are avoided or deliberately started as a pilot.
• Available data foundation: Internal data exists and is usable. Structure and access are clarified so that value can be created quickly.
• Internal advocates and visibility: Business units are on board, the benefit is easy to explain and visible in daily work. This creates buy-in and motivation.
• Low regulatory hurdles: For the starting point, highly regulated or particularly high-risk projects should be avoided.

In short: Good entry projects start small, deliver early results, and create the foundation to embed AI in the organization step by step.

Successful AI projects solve a concrete problem and create genuine value. They are built for productive everyday use — not for a pure demonstration or as an isolated solution.

The difference shows up in real operations. Successful AI projects are integrated into existing workflows, scalable for real user numbers and loads, and built to work reliably every day — even when data is incomplete, systems respond slowly, or conditions change. Operations, maintenance, and accountability are clearly defined and documented. IT security, data protection, and rights management are considered from the outset. The solution is cleanly embedded in the existing IT landscape.

Pure prototypes, on the other hand, often work only under ideal conditions. They run in test environments, depend on individual people, and are not designed for continuous operation. They are frequently isolated solutions without a clear architecture and no plan for maintenance, further development, or scaling.

In short: Successful AI projects are maintainable and scalable software solutions that hold up in everyday use. Pure prototypes show what's technically possible.

The greatest economic potential lies where high business value and repetitiveness come together — activities that occur regularly and where each individual run consumes time, money, or scarce resources. When AI supports or takes over such processes, measurable effects emerge. Typical examples are decision-making, review, assessment, research, forecasting, or planning processes that arise frequently and have so far been handled manually.

An important factor is the digital environment of the use case. AI is considerably easier to deploy when a company already operates digitally, data is available, and IT systems and interfaces exist.

In environments that are heavily shaped by physical assets, machines, or manual labor and have little digital data, processes must first be digitized and infrastructure built. Only with this foundation can AI deliver its intended impact.

Less suitable are use cases with low repetitiveness and low individual value. The effort for data, integration, and operations often doesn't justify the benefit.

Costs depend on scope, objectives, and complexity. We offer workshops, fixed project packages, retainers of varying scope and duration, and custom solutions — for different budgets and maturity levels. The most affordable entry point starts at €30 per month for a user license of our AI platform Galilea.