Sometimes, measuring the impact of design is hard.
Not every design decision can be tied to money or success right away.

With Almetra AI, the last 2 years of design partnership finally paid off. The team raised $19M USD in Europe and now continues to expand to the US.

In this post I wanna share how my team and I turned an early prototype into a scalable product that can be used daily. Keep reading until the end, as I will also share the one hack I learned to "corporate teams into daily users".

Confusing dashboards from Day 1

The Almetra Founders approached me after they got their first prototype live with a customer. They are building performance improvement software for manufacturing lines, so loads of data and visuals. The results were sobering:

• Hard to see what was going on in a manufacturing line
• The data was not as robust yet, and users lost trust
• Users didn't wanna adapt "yet another software"

With these problems in mind, we started a design journey that would not only shape the product, but also how the team approaches product design and user experience.

Overall, the journey included 3 pillars:

1. Talking to users, a lot
2. Design Critiques
3. Development-friendly designs

Pillar 1 - Talk to users, a lot

Manufacturing lines are complex. Most line leaders and process engineers don't have a good overview of what is going on in their line. While the team had experience in the industry, we were unsure about what the core users of the platform actually care about.

So we started talking to users. In Switzerland. In Poland. Remotely.

I was traveling from factory to factory to ask future users about their daily challenges. The key element I need to highlight here was: Non of the conversations we had included the software. I only asked about current challenges, and how they do "line reporting" right now.

Being genuinly curious was the reason I was able to understand how a 45-year old polish factory leader thinks about his problems.

Turning interviews into actual insights

Over the course of the next few months, we interviewed more and more users, and started collecting insights. The core learning here: Different roles need different dashboards. So we started dividing insights into roles, and map out what each role actually needs.

Pillar 2 - Design critique

We burned through a lot of concepts. Every new user story came with 3-5 possible options on how to solve a problem, or how to make something easier.

Here's how we made sure to pick a winning design:

1. I designed multiple options, and validated them with test users.
2. Then I prepared them in a presentation, walking through each option and it's benefits
3. Then I presented this either via a Loom or directly on a call
4. And the client was able to ask questions and pick a winning design

That way, we were able to:

• get an opinion from multiple perspectives
• get buy-in from leadership

After that, we went into design handoff and prepared the screens and user flows for the engineering team.

Pillar 3 - Development friendly designs

The beginning of the partnership between Almetra AI & Grauberg was all about figuring out what to build. During the last few months, we added a lot more thought into how we build things, and how to create a design system that works for thousands of companies, users & screens.

Since this is such a data-heavy software, a lot of charts needed to be designed & developed from scratch. Multiple states, loads of data and every chart also had to be interactive.

So we started building a design system, not only to make things easier for users, but also to increase development speed over time.

The 5 Key learnings

• Obsess over what to build, not how to build it. At least early on.
• Users only share their daily struggles if you show genuine interest
• A good design critique shows the best solution, and gets buy-in from the team
• The right time to build a design system is when engineers keep building the same components over and over again
• A good design system handles all possible states of a screen, the good and the bad

What the successful raise actually means

In the end, the successful funding round seems like a big bang, but it was the result of continously learning what customers need, design it and then build it.

Customers started getting actual results.

Users got faster completing tasks.

Software adoption within teams increased month over month.

The successful raise is the receipt of constantly designing and improving the software.

Here's what the CEO of Almetra AI, Max Fischer, said about our collaboration:

What this means for you

If you just launched a software or a prototype, and can't feel this "magical pull" from potential customers, then it's probably a design problem.

My team and I not only helped Almetra AI to turn confusion into product market fit and then a successful Series A, but also AI startups like Prosp AI, Siktio & Digimarc.

If you want to finally leave sales calls and user interviews with people being excited about your product, you should book a call with me here and discuss how we can get your software ready for Series A and beyond! I've only got a few spots available for next week, so be quick!