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AI-Powered System Optimisation

Achieve the optimal parameters: the Machinations Balancer empowers you to refine systems towards specific outcomes, eliminating extensive trial & error



Efficient exploration

Explore how a set of variables influence your target, with the minimum number of predictions


Identify optimal adjustments

Identify the optimal ranges within which a set of parameters work towards achieving your targets


Uncover correlations

Understand the relationships between variables and identify the control points of your designs

Efficient iteration

The AI Balancer automates the iterative and time-consuming process of fine-tuning parameters, for a faster and more streamlined optimisation process, allowing users to discover the most effective Influencer <> Target pairs.


Precise targeting

Optimise values for variables that you choose for a goal that you set. The AI evaluates the influence of each selected variable on the target, providing a more accurate and tailored approach to achieve the desired outcome.

Automation & simplicity

Reduce the complexity associated with manually adjusting variables and running numerous simulations. The Ai Balancer makes balancing accessible to a broader range of users without requiring in-depth expertise in simulation or optimisation techniques.


During the latest dry test we performed on a live game for one of our Alpha users, we set out to optimise players idle time. We were able to identify and prove a strong correlation between certain XP rewards and player idle time. Changing XP rewards params to the optimal one provided by the Balancer, resulted in a 46% improvement on idle time.

Harry Ashton

Architect, Machinations

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Eager to fly through the balancing process and make informed, precise and effective changes?


What is the Machinations AI Balancer and what does it optimise?

The AI Balancer is a module of the Machinations tool that optimises influencer variables within Machinations diagrams to achieve a desired target value. It leverages artificial intelligence to determine the impact of influencers on the target with minimal simulations.

How does the AI Balancer work to optimise influencer variables?

What are the benefits of using the AI Balancer?

Why was the AI Balancer developed?

What algorithms, models, and methodologies are used in the AI Balancer?

How was the AI Balancer tested?

What technical dependencies or requirements are necessary for using the AI Balancer?

How does Machinations ensure data privacy and security when using the AI Balancer?