What we've built fits cases that are most urgently tackled by ML or AI to reap benefits from this
technology. Our solution set goes very deep from data engineering to the use case and scope definition - creating value fast.
Golden Whale Plug & Play AI
Reaping the benefits of its early move into the AI space, Golden Whale's technology presents iGaming businesses with significant productivity gains across every level of operation. Eberhard Dürrschmid, CEO, explains how Golden Whale's modules can attach to legacy systems and modern data engineering infrastructures alike.
How has the gaming industry's attitude towards machine learning and AI changed since Golden Whale was founded in March 2022?
Te change has been profound, particularly in iGaming. When Golden Whale was founded pre-ChatGPT there was some interest, but to a certain extent our potential clients viewed AI as some kind of black magic or something that's very much in the future. With ChatGPT everything changed. Everybody knew what this new tool is and it's improving rapidly. Every company that wants to stay relevant and run operations economically will need to use this technology to a certain extent.
How has the sophistication of these technologies changed?
At every level and quickly - so quick in fact that we have a room full of experts adopting to the speed of change we are experiencing. Tis goes through every layer of the technology including data engineering where we constantly learn how to tackle huge volumes in real time and more reliably than ever before, model performance, model variety. Ten you get into the complexities of system overhauls and how to implement AI across operations. All of this brings huge productivity gains, easily getting to 100-200 per cent across simple use cases.
What early mover advantages does Golden Whale have in the increasingly competitive AI space?
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We've created a team and a technology that is adapted specifically to the industry. We can come into almost any iGaming operation and are ridiculously easy to work with because we attach to almost everything that's built, whether it's a legacy system or a modern data engineering infrastructure. What we've built fits cases that are most urgently tackled by ML or AI to reap benefits from this technology. Our solution set goes very deep from data engineering to the use case and scope definition - creating value fast.
How does Golden Whale's tech compare with what it was 12-24 months ago?
When we last spoke a couple of years ago a lot of the concepts were theoretical. We now have a large suite of results that prove what we are talking about and show the real value of the technology. Whereas we might have once spoken about model building and how to build a model around a certain problem, we can now talk about model building solutions, model engines, ongoing model adaptation, and so on.
From that, then we go can go into agentic workflows. We are now building agentic workflows around the core of our models to tackle operational and automation tasks. AI and ML can help us to increase productivity of those systems massively.
How do integration processes differ depending on a) the size of the
EBERHARD DÜRRSCHMID CEO Golden Whale
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