Modl
Revolutionizes game development with AI-driven testing and player experience enhancement.. [Contact for Pricing]
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What is Modl?
modl.ai is an AI-powered game testing and QA automation platform that uses AI agents and analysts to find bugs, glitches, and performance issues before players do. The platform offers an integrationless solution that requires no SDKs, plugins, or code changes—QA teams simply upload a game build and define test tasks in plain language like
Modl pricing
Pricing model: Freemium
modl.ai does not publish public pricing tiers on their website. Pricing is enterprise/custom and requires contacting modl.ai sales for a quote. The platform offers custom pricing based on usage volume and selected features. There is no publicly available free tier or free trial. The company secured $8.4M in Series A funding led by Griffin Gaming Partners and Microsoft's M12 venture fund to scale operations.
Modl pros
- No SDKs, plugins, or code integration required
- Test tasks defined in plain language, no scripting needed
- AI agents work independently without engineering involvement
- Automatic bug reports with descriptions, visuals, and severity scores
- Detects visual glitches, missing assets, and performance issues
- 24/7 continuous testing without manual intervention
- CI/CD pipeline integration for automated test triggers
- Custom-trained model for each game's unique visuals
- Model training handled on modl.ai side, no manual labeling
- Supports Android and desktop platforms natively
- Works across game engines including Unity and Unreal
- Captures video, logs, and performance data during tests
- AI agents adapt to non-deterministic gameplay changes
- Feedback system to continuously improve agent accuracy
- PlayStation Tools and Middleware program member
Modl cons
- No public pricing tiers listed, contact sales required
- iOS support still in development, not yet available
- Best for mobile games, PC/console support expanding
- Very fast-paced or timing-critical gameplay not fully supported
- Limited to game development industry only
- Agent focuses on testing not winning, skill limitations exist
- Model refresh needed for major visual overhauls
- No free tier or free trial publicly available
Frequently asked questions about Modl
How does modl.ai's AI testing work without integration?
modl.ai uses a black-box approach where AI agents observe and interact with the game purely through visuals. The system uses visual models and OCR to understand what's happening on screen—reading text, UI elements, and game states just like a human tester would. Agents send simulated inputs just like a player would. No SDKs, plugins, or code changes are required, so QA can start testing immediately without involving engineering.
What platforms does modl.ai support?
modl.ai currently supports testing on Android and desktop platforms. iOS support is in development. Console testing and additional PC game workflows are also being expanded. The platform joined PlayStation's Tools and Middleware program to support developers building games for PS5 and PS4 consoles.
What types of games is modl.ai best suited for?
modl.ai's AI currently excels at testing mobile games and titles with structured interactions or clear UI elements such as match-3, narrative, card, or turn-based games. The platform is actively expanding support for PC and console games. Very fast-paced or timing-critical gameplay isn't fully supported yet, but it's an area of active development.
What bugs and issues does modl.ai detect?
modl.ai identifies visual glitches, missing assets, performance drops, and gameplay logic issues. During each test run, the system processes available log files to surface exceptions and errors, and tracks device or platform performance data. This includes crashes, broken menus, missing assets, softlocks, and performance drops. Each report includes a description, evidence (screenshots, videos, logs), tagging, and an AI-generated severity score.
How long does it take to train the custom model for my game?
The initial custom model training typically takes less than a few days. Each game benefits from a custom-trained model that helps the AI recognize its unique visuals and UI. This training is handled mostly on modl.ai's side and uses data from test runs, so your team doesn't need to prepare or label assets manually. Updates to the model are automated as your game evolves.
Can modl.ai integrate with my CI/CD pipeline?
Yes. The system can be triggered automatically as part of your CI or build pipeline, allowing tests to run on new builds as soon as they're available. Results and reports are generated and accessible through the dashboard or can be pushed to your QA tools for review. You can start runs directly from the dashboard or trigger them automatically.
How does modl.ai handle game updates and content changes?
The system automatically adapts to most game updates and content changes. In some cases, such as major visual overhauls or new gameplay features, modl.ai may refresh the game-specific model to maintain accuracy. This process is largely automated and requires minimal input from your team. Users can also submit feedback on agent behavior or analyst results to help continuously improve accuracy.
Can the AI agents adapt to non-deterministic gameplay?
Yes. The agents use large language models to reason about changing conditions and can adapt to non-deterministic gameplay. This allows them to respond intelligently to variation in outcomes, UI states, or player paths. The agents use a growing library of skills such as navigating menus, identifying game states, and performing in-game actions to simulate real player behavior.
Does the AI agent try to win the game during testing?
It depends on the game. The agent's goal is to test, not to win. It focuses on verifying functionality, performance, and logic rather than mastering gameplay. For test cases that require advanced player skill or intuition, human testers remain the best fit. The agent's purpose is to identify bugs and issues, not to achieve high scores or complete challenges.
What's included in each bug report?
Each report includes a description of the detected issue, evidence (screenshots, videos, logs), tagging, and an AI-generated severity score which QA can review or adjust as needed. The reports highlight crashes, broken menus, missing assets, softlocks, and performance drops. These detailed, actionable insights help teams quickly prioritize fixes and maintain a high-quality experience.