ConSol

I’m the Managing Director of Bauer Sanierungen. We repair fire, water, and mold damage, and we also handle services related to drying, plumbing, heating, as well as painting and coating work. As managing director, I make sure our job sites run smoothly, our teams are deployed effectively, and we work efficiently and economically.

We ran into a real problem with scheduling. Our drying and trades teams were planned mostly manually, sometimes across split tables. It was hard to keep track of and kept leading to unnecessary trips and extra coordination effort. We also looked at standard solutions on the market. The issue was that none of them really understood—or captured—how our day-to-day operations work. On paper, some things looked like a match, but in practice key workflows, dependencies, and special requirements were missing. So it was clear that we’d need our own solution. In the past, this kind of custom development would likely have been too expensive and too time-consuming for us. ConSol showed us that with AI support, you can develop tailored software much faster and at significantly lower cost today. That’s what made a custom solution realistic for us in the first place. Our goal was a simple, well-fitting tool for our dispatch team. It should help us stay on top of things, plan the teams more effectively, and avoid empty drives.

ConSol quickly understood what we’re about. We didn’t need some big, off-the-shelf software package—we needed a solution that fits our day-to-day reality. What mattered most to us was that ConSol took our actual workflows seriously. This wasn’t about forcing our operations into some ready-made system. It was about building a tool that supports our planning in the way our real business requires. We were especially impressed by how much faster and more cost-effective it was to develop the software with the help of AI than before. For us as a trades business, that was a key point: we didn’t want years of an “IT construction site” or a massive budget—we wanted a practical solution that pays off economically. ConSol made exactly that possible.

First, we reviewed together how our planning was going so far and where the problems were. It wasn’t just about creating polished process diagrams—it was about real day-to-day work: which teams are available, which assignments depend on each other, where empty runs happen, and where we’re losing time in the coordination. After that, we developed a planning and dispatching tool step by step, tailored to our workflows. ConSol used modern AI tools in the software development process, which helped us get visible results much faster than we would have expected from traditional software development. We were also able to give feedback whenever something still didn’t quite fit. Those points were picked up right away and improved.

On our site, the management team and the responsible people from planning were involved. I personally was mainly there to make sure the solution makes sense for our operation. A technical contact and developer from ConSol were also involved.

We have a much clearer picture of the planning today. It’s easier to coordinate the routes, and we were able to cut down on empty trips. That saves time, reduces stress, and lowers costs. What mattered most to us was that the tool fits our way of working and isn’t just an off-the-shelf solution. With standard software, that wasn’t possible before. Another important point is that we’ve seen that custom software doesn’t have to be automatically prohibitively expensive or painfully time-consuming anymore.

The collaboration was straightforward. Our feedback was taken seriously and incorporated quickly.

What stood out most was that our real day-to-day business requirements were at the center of it. That’s exactly where standard solutions had failed before. But what surprised me most was how quickly, with AI support, individual software can be created today. In the past, for a custom solution I would’ve immediately thought of high costs, long timelines, and a lot of coordination. This project showed that things can be different now—if the service provider brings the right tools and the necessary know-how.

I can’t think of much more. Maybe we could define upfront a bit more precisely what success we want to measure later—for example, fewer empty trips or less time spent on planning. Overall, we’re very happy with the result and the collaboration.

Consult for Rare supports companies in the pharma and healthcare space with strategic questions related to rare diseases. As Managing Director and consultant, I’m responsible for client projects, business development, and the day-to-day management of the company.

I wanted to use modern AI tools in a targeted way for my day-to-day consulting work. My focus was on researching and organizing complex information more efficiently, creating presentations and concepts, and streamlining administrative tasks.

What made the difference was the hands-on focus and the ability to translate the potential of AI into concrete use cases in my day-to-day work. From the start, the program centered on the real challenges I face in consulting and management—not generic training content.

At the beginning, my existing workflows were analyzed and suitable areas for AI use were identified. After that, specific use cases were developed and implemented together in multiple coaching and sparring sessions. Over the following period, the AI use was continuously expanded and adapted to new requirements. The collaboration is ongoing to this day.

The collaboration took place directly between me, as the managing director, and the responsible AI consultant.

KI is now a permanent part of my daily work. In particular, when researching, structuring complex content, creating presentations, and handling administrative tasks, I can clearly save time. At the same time, the quality and consistency of many of my work outputs have improved.

The collaboration was consistently reliable and flexible. Feedback was taken on right away and integrated into further guidance.

What stood out as especially positive was the ability to translate technological possibilities into concrete, immediately usable ways of working. Instead of theoretical discussions, practical solutions were always at the center.

The collaboration was extremely successful. As the AI landscape continues to develop, new opportunities are constantly emerging—opportunities we can evaluate and explore together.