AI consulting, in short
For us, AI consulting means this: we check where artificial intelligence concretely saves time or money in your business, and we build exactly those cases. No hype, no slides that end up in a drawer. It starts with a potential check on your own processes, then a small proof of concept, and only if that holds, a production system. The difference from many providers: we do not only advise, we build it with an in-house development team. The contract, liability, and responsibility sit with us. Who this page is for: owners and IT leads at small and mid-sized companies who know AI is relevant, but not where to start or whether the effort pays off.
Where AI actually makes a difference
There is a lot of talk about artificial intelligence. In the day-to-day of most companies the potential stays unused, because three questions are open: where to start, which processes are suitable, and how to measure whether the investment pays off. These hurdles come up again and again in practice: A crowded market. Tools and models grow daily. Without technical know-how it is hard to tell what holds up from what is just hype. Data. Many AI efforts fail not on the technology, but on unstructured data, silos, and missing interfaces. Know-how. Machine learning, language models, RAG, and vector databases are specialized skills that internal teams often lack. We come in exactly here, with a sober view of where AI brings you something and where it does not.
Pure AI consulting or consulting with delivery?
Many AI consultants deliver an analysis and a roadmap. What comes after, the actual build, you have to organize yourself or hand to a third party. We do both from one place.
| Criterion | Pure AI consulting | Hanse: consulting and delivery |
|---|---|---|
| Result | a slide deck and a roadmap | a running system in operation |
| Delivery | on you or third parties | our own development team |
| Responsibility | ends with the recommendation | from the idea to operation |
| Contract and liability | often spread out | in one place, with us |
| EU AI Act | a note on the side | built in technically |
Our approach: from potential check to production system
We move step by step, so you see early whether an effort pays off before larger sums are invested. 1. Potential check. First, an honest inventory: which processes suit AI, where the biggest leverage is. From that comes a prioritized list of concrete use cases with a realistic effort and value estimate. This first check is free. 2. Proof of concept. Instead of a large, risky project, we start with a clearly scoped PoC. That quickly shows whether the solution works in your environment, with your data and your people. 3. Production. From a successful PoC we build a production-ready system: data integration, model or API integration, and embedding into your existing workflows. 4. Operation and monitoring. AI systems are not static. We set up monitoring, logging, and feedback loops so the solution gets better over time. Agentic AI. It gets interesting when AI does not just answer single questions but plans and runs multi-step workflows. A customer request from triage through research to a reply. We build first workflows in this direction today, and for many companies this is the next automation step.
What we work with
We pick the tools by use case, not by hype. The focus today is on language-based AI and automation: Classic machine learning, such as custom forecasting models, we use where enough structured data exists. What makes sense in your case we clarify upfront in the potential check.
- Large language models for text understanding, generation, and assistance
- Retrieval Augmented Generation (RAG), so the AI answers from your own documents with facts
- Chatbots and assistants for customer service, internal helpdesks, and lead qualification, connected to your CRM or ERP
- Document and ID recognition via OCR
- Process automation for recurring tasks
- Python as the base, plus vector databases for semantic search
What we have already built with it
Anyone can talk about AI. We would rather show what already runs. ID recognition. Our own product reads IDs via OCR and takes over the data automatically, instead of typing it in. AI for hospitality. For hotels and restaurants we run AI-supported automation, from the request to the reply. FeWo.de. A complex booking platform we built ourselves and operated for years. Not a pilot, but a product with real users. If you need custom software with AI features, there is more on our software development page.
EU AI Act: considered from the start
The EU AI Act regulates the use of AI and largely takes effect from August 2026. It affects anyone who uses or develops AI, whether built in-house or used as a third-party solution, if it touches the EU market. We build AI with these requirements in mind from the start: risk classification, technical documentation, transparency toward users, and, where needed, a human in the loop. So your solution is not only usable today, but also prepared for full effect from August 2026.
Frequently asked questions
What does AI consulting cost?
The first potential check is free with us. What the build costs depends on the use case, the state of your data, and how deeply the AI is embedded into existing systems. A lean proof of concept is far below a production-ready system with full integration. You get a reliable estimate after the check, before you decide.
Where should we start?
With the process that hurts most and is well documented. In the potential check we look at your workflows and prioritize the use cases by effort and value. You start where the leverage is largest and the risk is smallest.
What data do we need to use AI?
It depends on the use case. For language-model applications like chatbots or document analysis, your existing documents, manuals, or FAQs are often enough as a knowledge base, no custom training data needed. For specialized forecasting models you need structured historical data. What is possible we see in the potential check.
What is agentic AI and how does it differ from a chatbot?
A chatbot answers single questions. An AI agent breaks a goal into steps, uses external tools and data sources, and runs multi-step workflows largely on its own. Example in customer service: take the request, research internal knowledge sources, check the order status, draft a reply, and escalate to a person if needed.
Do we have to deal with the EU AI Act?
As soon as you use AI, very likely yes, if your solution touches the EU market. We start with a risk classification of your planned application and derive the concrete obligations: documentation, transparency, and, where needed, human oversight. We build these mechanisms in right away.