We Harness AI to Boost
Your Revenue
AI-powered personalized game balancing and content creation for player clusters in real-time to maximize game retention and monetization. Give players a superior gaming experience and secure high engagement rates.
Trusted by Forward-Thinking
Teams Around the Globe
Grab the opportunity to join the best
GameAI™ is a bespoke, game-changing ecosystem of instruments designed to ensure your success in the market.
We advance every stage of the game development cycle for your progress.
Playtesting & Balancing
Fast & smart AI agents play and explore games
at scale to improve user experience quicker
and more accurately than human playtesting.
Retention & Monetization
Maximize retention & in-app and ad revenue with
the real-time progression and level complexity
curves for a various players’ groups in correlation
to monetization metrics.
Real-Time Personalisation
Balance levels in a personalized way to engage
an exact user and advance his/her experience in
your game.
Scale Your Business
with GameAI
We help you fast-track your game development to grow your revenue by keeping what matters to you most front and center: accuracy, speed, intelligence, ease, cost-effectiveness and data-backed insights.
Everything you
need in one place
Precise, data-backed
results with up to 90%
Over 20x faster than
AI players of any
skill level at your
service, 24x7x365
Improved user experience
due to visualised variety of
strategies, telemetry and
Save time & money by up to
70% thanks to AI handling
routine jobs
Use Cases
Read about the success stories from our clients.
Learn how GameAI™ helps boost game
development and automate routine tasks.
Effortless Integration
We suggest different ways of integration,
each of them requires minimum
resources on your end. It takes no more than 5 days.
SDK (Unity Plugin)
Game Source Code
  • What is GameAI™?
    GameAI is an AI suite of instruments designed to help you leverage accurate and unbiased game testing & balancing as well as a real-time personalisation of level complexity and content for your players to give them the best fully customized experience in your game. To show the impact and predict future transactions we connect balancing and game content usage parameters with revenue and retention metrics.
  • Who is GameAI™ intended for?
    Game developers who are looking to search for ways of improving user experience, securing high engagement rates, and maximizing retention to meet financial goals and accelerate revenue.
  • How does GameAI™ work and what technology does it use?
    We apply Reinforcement Learning, as well as other Machine Learning algorithms like generative adversarial networks and clustering algorithms to model multiple diverse scenarios for balancing the game and content generation, user behaviour, retention and revenue prediction. Such a cocktail of RL and ML is selected to achieve the best possible results as it provides us with the necessary artificial intelligence to be able to mimic real users behaviour and decision making and by aligning with the major monetization trends provide personalized well balanced game content in real-time which leads to critical game metrics increase. Predicting in-game and ad revenue is performed by another set of ML algorithms.
  • What are AI Agents?
    AI Agents are virtual agents taught and trained with the help of Reinforcement Learning to achieve different goals, from beating the game, performing a unique set of tasks, enhancing gameplay immersion, to helping improve the gaming development experience and balance, including retention and monetization. In short, AI Agents can be used for:
    • Automated Content Generation
    • Game Testing & Balancing
    • In-Game Player Avatars (Mimicking real users up to 99% of accuracy)
    • QA automation
    • And much more.
  • How do we train AI Agents?
    To train AI agents efficiently, we developed a unified training pipeline. Here’s the 3-step pipeline:

    Determine the action space. These actions can be discrete, like pressing a button or continuous as in the value within an interval. Actions can be as many as you’d like to have performed simultaneously, and keep in mind that some actions can be invalid under specific conditions.

    Determine the reward. The reward must correspond to the goal we want to accomplish—for example, a positive reward when winning and a negative reward when losing. You can also give a positive reward for each action that leads to a target or a negative reward when moving far away from the target.

    Define the Deep Reinforcement Learning algorithm. The speed of the agent’s learning capabilities depends on the algorithm as well as whether the agent can learn at all. For each task, it’s necessary to study the features and how applicable they are in unique contexts.

Get Superiority
with AI on Your Side
You do creativity - GameAI™ does all the rest
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