Our pure application of machine learning delivers deep insight into operational efficiency, with accurate, actionable advice and measurable financial gains.


About 90 percent of everything we buy will travel on ships ↗︎. This is because shipping goods by sea is efficient and economical. That, however doesn’t alter the fact that the marine industry uses a lot of fuel and operating vessels is expensive. 

You know that operating your vessels more efficiently is important. It saves fuel and that means money. It’s greener too which is good for all of us, and our children. It is also true that achieving big improvements in operational efficiency, in a way that minimises investment risk, is possible. 

Imagine if you didn’t need to invest in operational advice until you knew what level of savings you were going to make. We have a strong focus on data-driven solutions because we know that hardware-based solutions aren’t for everyone. Imagine if you could make significant improvements to operational efficiency without needing to install anything on the vessel and without needing to integrate to on-board systems.

What if you could use your historical vessel data to identify where your increases in efficiency were going to come from? Machine learning is technology that thrives on scale. It’s technology that can make sense of the vast amount of data inputs and use the outcomes to make accurate predictions on where the inefficiencies are. 

GreenSteam is the only company using machine learning technology in the area of vessel operational efficiency.

2007 Founded in the Faroe Islands

2011  Company HQ moved to Copenhagen

2012 Reached 1 million lines of code

2014 Signed partnership with Castrol

2016 Opened product development office in Poland

in Europe
and growing
terabytes of
data analyzed
metric tones
of fuel waste identified


Most operational advice that aims to increase vessel efficiency isn’t all that accurate. In practice, that means it can actually reduce efficiency. Which will waste even more fuel.

We are at the forefront of using machine learning to analyse as much data as possible from your vessel and how it operates. The more the better – more inputs, longer timeline. The more data we can feed into our machine learning system, the more we can learn about how the vessel operates in as wide a range of conditions as possible. This translates into more accurate predictions about how it will perform in the future, in similar conditions… Or, crucially, in conditions not yet encountered. It also enables us to spot operational trends that can identify inefficient or damaged vessel technology. 

Only machine learning can make sense of all the data needed to make accurate predictions. 


We started GreenSteam back in 2007 as three Technical University of Copenhagen PhD graduates, and two of us are still here. Being from the Faroe Islands, the marine industry was very close to our hearts and we could see that there was enormous, unexploited potential in using our machine learning knowledge in addressing some of its big challenges. One of the most significant problems to solve was the fact that ships use a lot of fuel, and those ships are pretty inefficient in how they use it. We could see that there were quite a few startups looking at marine fuel efficiency, but to us, we only had to look at vessels bobbing about on the ocean to see that machine learning was the logical technology to address this big, green, challenge.

After several years of intense development and testing with innovators and early adopters, our core machine learning technology matured. Alongside this, GreenSteam steadily grew too and we launched our first product, GreenSteam Optimiser in 2011 and immediately signed our first customer. Since then, we’ve seen dramatic growth, opening offices in Poland and the UK, and enjoyed commercial success across all vessel types and classes.

In 2013, Castrol invested in GreenSteam having looked at a number of companies that would strengthen their drive for using technology to improve operational efficiency. We’re pleased that they saw us, with our machine learning technology, as being a credible answer to the challenges of improving operational efficiency. So successful have we been in this area that they increased their holding to 60%. That said, we’re still an independent company – but one that works with Castrol to bring the combined benefits of our approach to customer operations. Castrol’s ongoing investment gives us global reach and has enabled us to grow to over 20 staff in three geographies.


We are always looking for talented people to join our team.

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