A Major Milestone On Our Mission To Enable Manufacturing Efficiency With AI

When I decided to leave my cozy job at Bosch in the middle of the pandemic, and with a baby on the way, to create my own startup, I knew that the road ahead would be tough. Over the past few years, I faced every challenge with a clear vision in mind: making manufacturing more sustainable through on-demand expertise powered by artificial intelligence (AI). We've come a long way from our first Python-based prototype, and we're now helping manufacturers increase their productivity by 20% on a daily basis. Today, I'm happy to announce that GaussML successfully raised a €390,000 angel funding round from strategic investors with deep expertise in manufacturing and artificial intelligence.

This investment comes from a remarkable group of individuals who bring far more than capital. Our investors are directors of manufacturing companies, serial entrepreneurs, ex-VPs from giants like Autodesk and SodaStream, and experienced sales directors and CFOs. Their combined expertise in manufacturing operations, AI solutions, and scaling startups provides GaussML with strategic guidance that will prove invaluable in our next growth phase.

At GaussML, we feel lucky to have successfully raised this funding at a critical time for the manufacturing industry. Companies worldwide face an unprecedented skills shortage, with invaluable manufacturing expertise literally walking out the door as experienced operators retire. At the same time, market uncertainty, rising energy costs, and supply chain challenges are putting immense pressure on production efficiency. This creates a perfect storm where manufacturers must leverage cutting-edge AI solutions to remain competitive.

With this investment, we're boosting our mission to democratize manufacturing expertise through AI. The capital allows us to expand our team with key hires in sales and engineering, accelerate our product development roadmap, and support our European expansion through strategic partnerships. Most importantly, it lets us continue refining our unique "small data" approach together with our customers, delivering tangible results after just a handful of experiments, and bringing expert-level performance to any operator, on any machine.

The Manufacturing Expertise Crisis

Recent policy changes by major economic powers have introduced substantial uncertainty into world markets. Manufacturers are navigating an environment where tariffs and counter-tariffs are announced and cancelled with little warning, creating volatility in supply chains that were previously stable and predictable, and leading to considerable price fluctuations in raw materials and finished products. These swift market changes are forcing manufacturers to reconsider sourcing strategies and production locations at a time when they already face unprecedented challenges in maintaining their operational excellence.

For decades, manufacturing companies relied on experienced operators—masters of their craft who could hear a machine running from the parking lot and notice if something was off. These skilled workers accumulated years of expertise, learning how to fine-tune machines for optimal performance through countless hours of trial and error. As these experts retire, companies are experiencing a know-how drain that leaves them operating their machines way below their peak potential.

The traditional approach to process parameter optimization cannot keep up in the digital and AI era. Each material, product, and machine combination requires specific process parameters. When a new machine, material or product arrives, the process starts all over again. Quite often, a machine operator, supervisor or a process engineer spends weeks on-end running experiments to find decent parameters for each manufacturing task. If they're overwhelmed with other tasks, they might simply rely on old parameters or conservative ones provided by the machine manufacturer. Either way, the result is the same: suboptimal performance that wastes resources and limits productivity.

Running an efficient production line can make or break a manufacturing company. We've seen, through countless customer interactions, that their industrial machines are usually running at just 80% of their potential productivity. For a medium-sized manufacturer with ten machines, this translates to leaving €500,000 of annual productivity on the table. Multiply this across the manufacturing sector, and we're talking about billions in untapped productivity. Manufacturers need cutting-edge tools now more than ever, to run their machines efficiently and remain competitive.

Since OpenAI launched ChatGPT, we've seen an explosion of AI solutions flooding the manufacturing market, promising automated quality control, lower downtime with predictive maintenance and many other applications. Most of these cost-intensive solutions require IoT sensor installations, months of data collection, and complex integration with existing systems. But let's be realistic: most manufacturers can't afford to spend time and money in lengthy implementation projects with uncertain outcomes. They need solutions that deliver immediate results with minimal risk—especially when they're already struggling to find enough people to keep their production lines running.

Our work with Steinhart Metallwarenfabrik since 2022 is a beacon of what's possible when manufacturers with the right mindset are given the right tools. Before implementing our solution, their operators spent hours manually fine-tuning cutting parameters for each new task. The process depended heavily on operator experience, and often resulted in wasted productivity and time-consuming rework. After implementing Optimyzer, they achieved a 16% increase in productivity and drastically reduced the time operators spent on manual rework. As their managing director told us, "The smart Optimyzer is characterized by its ease of use"—exactly what manufacturers need in today's challenging environment.

Our "Small Data" Journey

When most people hear "AI," they immediately think of massive datasets and expensive infrastructure. The common wisdom is that "data is the new gold" and that artificial intelligence needs thousands — if not millions — of data points to make meaningful predictions. But this assumption isn't always true, especially when you have the right approach to the problem.

My journey with "small data" AI optimization for manufacturing began during my postdoc at the Inria Research Institute in France. I worked on a fascinating challenge: teaching humanoid robots to quickly understand the differences between their internal physical models and how they actually behave in the real world. Traditional approaches would have required hundreds of expensive experiments, which wasn't practical. We needed a solution that could deliver results within just a few movements of the robot.

This constraint pushed me to develop algorithms that could extract maximum value from limited data points. Instead of relying on classical neural network models, I built smart digital twins that understood how physical systems work and could tell when they don't know how the system will behave. The results were remarkable — the robot could adapt to the real world with just a handful of carefully selected experiments.

When I joined Bosch's Center for Artificial Intelligence, I had the opportunity to apply similar thinking to industrial process optimization challenges across various manufacturing domains. The parallels were striking — both robots and manufacturing machines are expensive pieces of equipment that operate in complex physical environments where small parameter adjustments can lead to dramatically different outcomes. The experience reinforced my belief that a "small data" approach could revolutionize how manufacturers optimize their processes.

I left Bosch in August 2020 and started developing the first version of Optimyzer, our AI copilot for manufacturing right away — a bare-bones algorithm without any user interface. Those early days were challenging, coding late into the night while preparing for the arrival of my first child. The initial results were promising enough to convince a few machine manufacturers that this approach could deliver tremendous value to customers struggling with process optimization, which is how GaussML got the first results on real laser cutting machines.

The first user-friendly version of Optimyzer allowed customers to create isolated optimizations for specific materials and machines. While functional, it didn't leverage the full potential of our approach. The breakthrough came with our second major version, which connected optimizations across a machine to enable knowledge transfer. This unlocked the true power of our solution — after running a handful of optimizations on the same machine, Optimyzer could achieve excellent performance with its very first parameter suggestion for new materials or parts.

GaussML's “small data” approach received its first successful validation with sheet-metal processing companies like Steinhart Metallwarenfabrik and Haimerl Lasertechnik GmbH, who became our earliest champions. Their willingness to test an unproven solution from a bootstrapped startup gave us the real-world validation we needed. As word spread about our results, GaussML began attracting interest from larger organizations — Tier-1 automotive suppliers and even OEMs. With each new optimization, we gained valuable industry experience, helping us fine-tune Optimyzer to deliver ever-faster results for our customers.

Our strategic partnership with Würth Italia, announced earlier this year, marked a pivotal moment in our growth journey. As one of Europe's leading industrial suppliers with over 40,000 customers in Italy alone, Würth provides us with a distribution channel that would have taken years to build on our own. Their team of experts understands the day-to-day challenges faced by manufacturers and can identify the processes where Optimyzer will deliver the most value.

The journey from concept to market-ready product has reinforced what makes our approach unique: while other AI solutions require months of data collection and integration, Optimyzer delivers tangible productivity improvements in a single afternoon. By focusing on quick wins that generate immediate ROI, GaussML has built trust with customers who are often skeptical of lengthy AI implementation projects with uncertain outcomes.

Strategic Investors Provide Much More Than A Cash Boost

Raising capital for a startup is never just about the money. It's about finding the right partners who won't just "cheer from the sidelines", but actively support the founder's vision with their expertise and network. After successfully bootstrapping GaussML through our critical validation phase, we chose to work with business angels who could provide more hands-on support and industry-specific connections.

Our €390,000 angel round, closed in April 2025, provides much more than working capital. We are extremely proud to have won over a remarkable group of strategic investors with deep expertise in manufacturing, AI, and scaling technology companies. They are manufacturing directors, serial entrepreneurs, and seasoned executives who strongly believe in our vision and understand the challenges that GaussML is solving.

Our angel group includes veterans from the technology and AI sectors. Ben Schrauwen and Samir Hanna, both former executives at Autodesk with multiple successful startups under their belts, bring invaluable experience in scaling AI companies. Their background in building software solutions for the manufacturing sector aligns perfectly with our mission to make manufacturing expertise accessible to all.

"I invested in GaussML because of the strong technical mentality of the founder, the competitive edge of their small-data approach, and their validated business model. Having followed Jonathan since we met at an accelerator in 2020, I was convinced by GaussML's early successes and knew the team would drive this company to excellence." — Mario Turić, entrepreneur and angel investor

They say it takes a village to raise a child, and thanks to this round of investment, GaussML now counts on many new supporters who are eager to open doors to new customers, provide guidance on market positioning, and help us navigate the complexities of enterprise sales cycles. They're also instrumental in preparing us for our next fundraising stage - a seed round planned for 2026 that will fuel our expansion beyond the DACH region and Italy into the broader European market.

With our Würth Italia partnership gaining momentum and growing interest from multinational manufacturers, GaussML is positioned to capitalize on the market opportunity immediately. This funding gives us the resources to respond to this demand while continuing to refine our technology and develop new capabilities that extend our competitive advantage.

The Road Ahead

This funding round marks the beginning of an exciting new chapter for GaussML. While GaussML made remarkable progress as a bootstrapped startup, this capital influx allows us to accelerate our vision for more sustainable manufacturing.

Here's how we're investing this capital to maximize our impact:

First and foremost, we're expanding our team with strategic hires in both technical and commercial roles. Stefano Chiavegati, who joined as a freelancer in 2023 became our first official employee in April 2025. We aim to build a technical sales team that understands the challenges that manufacturers face and the transformative potential of our technology.

On the product front, we're extending our AI capabilities to deliver even more value to manufacturers. At GaussML, we always aim to make running optimizations easier and faster. We're strategically leveraging external contractors to accelerate this development while building our in-house capabilities, ensuring GaussML maintains its technological edge in the "small data" approach to manufacturing optimization.

These investments align with our ambitious goals for 2025-2026: reaching many more customers and establishing GaussML as the leading manufacturing optimization solution across Europe. Our next funding round, planned for 2026, will further accelerate our expansion, allowing us to target additional European markets including France, the UK, Spain, and Poland.

The market opportunity before us is substantial - with millions of industrial machines worldwide. What excites me most is that we're already seeing validation of our approach with both SMEs and larger enterprises. Every manufacturer, regardless of size, faces the challenge of optimizing their processes and maximizing the value of their equipment. Our AI copilot offers a uniquely accessible way to solve this problem, delivering immediate ROI without complex implementation projects.

While Industrial AI might not receive the same attention as consumer-facing AI applications, the potential impact is enormous. By making every machine operator as effective as someone with decades of experience, we can ensure that every machine is run efficiently. That's the kind of transformative change that motivates our team every day, and with our angel funding secured, GaussML is better positioned than ever to deliver on this vision.

Join Our Journey

As we embark on this next growth phase, we're actively seeking partners who share our vision for a more efficient and sustainable manufacturing industry.

If you're a manufacturer looking to unlock the hidden potential in your machines, we'd love to show you how Optimyzer can deliver immediate productivity gains with minimal effort. For industry experts and sales professionals passionate about transforming manufacturing, we're building a team that will shape the future of industrial AI.

Interested in learning more? Connect with us on LinkedIn or reach out directly through our website. Whether you're a potential customer, partner, or team member, we're excited to explore how we can create value together.

Here's to making sure everything we make is well-made!

— Dr. Jonathan Spitz, Founder & CEO/CTO of GaussML