“We had to do a lot of convincing”

MAIA from Leipzig, founded in 2021 by Carolin Maier, Mathias Jakob and Moritz von Hammerstein, takes care of industrial product development. “We are giving this company a new colleague, MAIA. And this colleague is something very special,” says founder Jakob about the concept. Initially, the team wanted to build a kind of ‘Notion for industrial companies’. However, the product “did not meet market requirements”. This is how MAIA came into being.

In an interview with deutsche-startups.de, MAIA creator Jakob presents his company in detail.

How would you explain MAIA to your grandmother?
Imagine a huge company, grandma, that has been building complicated machines for decades. All technical knowledge is scattered in tens of thousands of documents and the minds of the most experienced employees. We are giving this company a new colleague, MAIA. And this colleague is something very special! Firstly, she has seen everything and doesn’t forget anything: we give her all the tens of thousands of documents to read. If someone has a question, she has the answer ready in seconds and also tells you exactly where she looked it up. That is the basis. Second: She can think for herself and combine things: Now comes the trick. She can also find knowledge that is not directly available anywhere. If one document says that component A can withstand extreme cold, and another says that it can also withstand enormous pressure, MAIA can automatically conclude: ‘A combination of these two parts would be perfect for a machine at the bottom of the sea!’ – even if no one has ever written it down like that. Third: But the most important thing is: MAIA listens and learns from its colleagues. She draws her knowledge not just from finished documents, but directly from the conversations she has. If a colleague gives her information in a conversation that hasn’t been written down before, or if – as in the component example – she recognizes connections between the lines, then she absorbs this knowledge. It extracts these new insights, documents them independently and makes them immediately available to everyone else. What was just in the head of one individual or hidden in the data is now a verified answer for the entire team.”

How do you want to make money, so how exactly does your business model work?
We rely on a very classic B2B SaaS model. Our focus is clearly on the enterprise solution for teams of 20 or more users. This starts at 59 euros per user per month
includes extended functions and personal support. For teams with fewer than 20 employees, we also offer a professional license for 49 euros per user – but here without direct support. In addition, if required, we offer workshops, enablement programs and in-depth advice – directly from our team or through selected partners. We see these services as support for a successful roll-out, but they only make up a small part of our sales. Over 98% of our revenue is recurring SaaS revenue from licenses.

How did the idea for MAIA come about?
The idea came about during a master’s thesis in collaboration between the University of St. Gallen HSG and the Hasso Plattner Institute HPI. The focus was on why so much time is wasted in industrial companies searching for product information. Carolin and I got to know each other through this master’s thesis and decided to further investigate the problem and develop a software solution for it. We then founded our company Prodlane at the end of 2021. Our first product was essentially a ‘notion for industrial companies’ – we wanted to create a central platform in which all knowledge was collected manually. Despite the first customers and initial success, it quickly became clear that the product did not meet market requirements and a pivot was necessary. At the same time, we had already developed a first version of an AI-supported search on our own data in the old product. Customer feedback on this one feature was overwhelming – much stronger than on the previous main product. So we decided to focus on this feature. Initially, MAIA was a generic AI search. But since we were already deeply immersed in the subject of technical documentation through our first product, we quickly realized that by specializing in the industrial sector and technical data, we could deliver a dramatically higher quality than general solutions. That was the birth of today’s MAIA.

What were the biggest challenges you have had to overcome so far?
Firstly, the pivot I just mentioned. We had to completely reorganize the team and develop and bring to market a new product practically overnight with very limited resources. Speed ​​was everything there. The second challenge was the level of detail in product-market fit. It’s not enough to know that engineers are looking for a long time. We had to dive deep and understand: Why exactly is it so hard? What do the workflows look like in detail? And most importantly: What are the common patterns among all our customers and where are there differences? Clearly reflecting this complexity in a standardized product was a tough nut to crack. And finally, at the beginning we had to fight against a double skepticism: Established industrial companies are naturally cautious – especially towards the hype topic of AI and then also towards a young startup. We had to do a lot of convincing to prove that our solution was safe and reliable. Fortunately, the tide has now turned and openness has increased significantly.

Which project will soon be at the top of your agenda?
On the product side, we are penetrating our niche even deeper. The big issue here is the extraction of implicit knowledge. We now work very closely with some large customers who give us deep insights into their structures and processes that were previously hidden from us. We use this feedback to develop functions that are even more precisely tailored to the way industry works. At an operational level, we are about to complete our ISO 27001 certification. This is a crucial step for us to further professionalize our organization and make collaboration with enterprise customers even faster and smoother. And strategically, we are preparing our next round of financing for the second quarter of 2026. The signals so far are very positive, but of course we know that there is always a lot of work ahead of us before the signature is signed.

Where will MAIA be in a year?
In one year, MAIA will have established itself as one of the industry standards for technical knowledge management – and will be used internationally beyond the DACH region. The fuel for this growth is our planned financing round at the beginning of the year. With the fresh capital we will really accelerate: We plan to almost double our team size in the next twelve months. This also includes expanding our physical presence. We will open at least one more location in Germany or Switzerland and at the same time hire the first employees in other European countries in order to be able to serve our international customers even more directly.

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Photo (above): MAIA