Revolutionizing Manufacturing with AI

How Ailoys is Bringing Industry 4.0 to Traditional Sectors

Industrial manufacturing is at a crossroads, particularly for small and medium enterprises struggling to keep pace with digital transformation. Berlin-based startup Ailoys is bridging this gap with an innovative approach: combining proprietary sensors and AI-driven digital twins to enhance production processes without requiring costly machinery replacements. We spoke with Co-Founder and CEO Sergei Altynbaev about how Ailoys is revolutionizing industrial processes and why Berlin has become their launchpad for global expansion.

Can you tell us about your background and what led you to found Ailoys?

My first degree is in Materials Science Engineering, and I recently graduated from the Executive MBA program at IESE Business School.

In my corporate career, I gained extensive experience in aerospace manufacturing, railroad engineering, and the renewable energy industry. Over time, my role gradually shifted from purely engineering-focused positions to business development. Understanding the value behind the engineering products and materials you sell to customers is crucial, and this transition allowed me to bridge both worlds.

But after 15 years in the corporate world, I started dreaming of something more agile. Thanks to the great advice I received, I began exploring the startup world. At first, the decision was hard, especially figuring out what product or idea to develop. But then, after discussing it with friends, I conducted a kind of SWOT analysis of myself and my network and continued brainstorming with colleagues.

One of my friends actually believed in my ideas and decided to support me. That's how I teamed up with my amazing co-founder, Albert Klein. Together, we initially explored the idea of metallic materials discovery. As it happened to be, the idea is not so scalable, but after a few interactions with our industrial connections, we refined this concept. This led us to develop our Industrial AI platform.

How does your technology work in practice and what results have you achieved so far?

In a nutshell, we install proprietary sensors directly onto our customers' machinery as part of our solution. These sensors allow us to create a highly precise digital twin of the manufacturing process. Then, using our Industrial AI platform, we can forecast how the process will behave under different technological parameters. As a result, we provide customers with what we call Operating Instructions, simple, actionable guidelines that operators can easily implement.

Imagine a production shop floor where a worker is operating manufacturing machinery. Let’s take the example of cable production from copper alloys. Every time a new alloy is introduced, or when an alloy contains impurities, workers must manually determine the optimal technological parameters to run the wire-drawing machine efficiently. By installing our sensors on customer machinery, we collect real-time data and use AI to generate optimized operating instructions in less than a second. More importantly, we can guarantee the performance we predict. This means we can issue a warranty for our Operating Instructions, giving our customers full confidence in their production output.

As an example, we recently onboarded the machinery of the world-leading wire-drawing equipment manufacturer, Kieselstein International. In recycled copper wire drawing, we successfully increased the wire-drawing speed by an average of 30%! Imagine the impact this has on inventory turnover, considering the price of copper.

How does your Digital Twin technology contribute to new materials development?

Thanks to our proprietary sensors, we can build a highly precise Digital Twin of the manufacturing process. The beauty of this technology is that these Digital Twins can be interconnected across an entire value-added supply chain.

With our technology, we can connect Digital Twins of manufacturing processes within the Ailoys Digital Manufacturing Dataverse (ADMD). This allows us to suggest material chemistries that deliver the best performance within the constraints of the existing supply chain, avoiding unnecessary capital expenditures.

This innovative approach is exactly why SPRIND (Federal Agency for Breakthrough Innovation) invited us to the DeepTech Berlin meets SPRIND event. There, we were encouraged to further develop our technology. Such recognition serves as a strong validation of our work and motivates us to push the boundaries of what's possible.

What specific challenges do you address for manufacturing companies?

It's very simple, we focus primarily on material manufacturing industries, particularly metallurgy, where we have the strongest domain expertise.

In industries like metallurgy, equipment manufacturers typically drive digitalization. However, many metallurgical companies still operate machines that have been in use for decades, even centuries, making equipment upgrades an extensive CapEx investment. This is where our solution is ideal, by leveraging our proprietary sensor technology and IoT platform, we can rapidly and efficiently bring our customers into the world of Industry 4.0, without requiring costly machinery overhauls.

SMEs, particularly in Europe, are under increasing pressure due to:

• Aging workforce
• Growing competition from global markets
• Rising energy costs
• Disruptive technologies like AI

This is precisely where our Industrial AI platform comes in. Our main goal is to help SMEs overcome these challenges by:

• Maximizing the performance of their existing manufacturing processes
• Accelerating time-to-market for competitive and sustainable material solutions

By doing this, we enable SMEs to remain innovative, competitive, and resilient, even in the face of rapid technological transformation.

How is Ailoys contributing to battery technology advancements?

We have entered into a cooperation with JR Energy Solution that is specifically aimed at accelerating Li-Ion battery innovation and bringing new battery technologies to market more quickly.

Batteries are a perfect application for AI, as they consist of three key components: cathode, anode, and electrolyte. Each of these components has a unique chemistry, and every combination of these chemistries results in distinct battery performance characteristics.

Ailoys' Industrial AI platform plays a key role by analyzing battery chemistry and performance data, providing precise technology for optimized battery designs. As soon as JR Energy is familiar with our Industrial AI platform, they can easily adopt our technological instructions and produce and deliver these optimized batteries.

Why did you choose Berlin as your base, and how has it benefited your company?

Berlin attracted us with its well-developed ecosystem for startups and innovation. One of the key connections for us was INAM (Innovative Network of Advanced Materials), which introduced us to Berlin Partner - a crucial organization that provided a single point of contact for all our business-related needs.

Over time, we even developed our own saying: "If you want to start a business, you can do it in many places. But if you want to boost-up your business, you need to go to Berlin."

The city's AI and deep tech ecosystem, combined with strong government support, networking opportunities, and access to industry leaders, has been instrumental in accelerating our growth and innovation.

What's next for Ailoys?

We plan to expand our IP further in the field of EDGE computing. Currently, our AI agents generate operating instructions from the cloud, but we want to enable their deployment via dedicated EDGE computing devices - about the size of a mobile phone. Our engineering team is already developing this solution, and we plan to release the first model in Q2 2026.

As for industries, we remain committed to manufacturing and are focused on onboarding new verticals to our Industrial AI platform. Expanding into new manufacturing domains will allow us to bring AI-driven process optimization to an even broader range of industrial applications.

Thank you very much for the interview!

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This is an abridged version of an interview that first appeared on ai-berlin.com.

Header image: Reve AI