About
modl.ai is a purpose-built AI game testing platform that empowers QA teams to automate test coverage without involving engineering. Its black-box approach means the AI agents observe and interact with games purely through visuals — using computer vision and OCR to read UI elements, game states, and on-screen text — so no SDKs, plugins, or code hooks are ever needed. Teams simply upload a build, describe their test scenarios in plain language (e.g., "Complete the tutorial" or "Reach level 5"), and the agents autonomously execute those tasks, capturing video, logs, and performance data throughout each session. After each run, built-in AI analysts automatically generate detailed, actionable bug reports that surface crashes, broken menus, missing assets, softlocks, and performance drops — complete with severity scores and attached visuals to help teams prioritize fixes quickly. Runs can be launched manually from the dashboard or triggered automatically as part of a CI pipeline for continuous quality monitoring. modl.ai currently supports Android and desktop platforms, with iOS support in active development, and excels at testing mobile, narrative, card, match, and turn-based games. It is ideal for mid-size to large game studios looking to scale QA coverage, reduce release-blocking bugs, and ship on schedule with greater confidence.
Key Features
- Zero-Integration Testing: AI agents test your game externally using visual models and OCR — no SDKs, plugins, or code changes required. QA teams can start immediately without engineering involvement.
- Plain Language Test Instructions: Define test cases the way you think: write instructions like 'Complete the tutorial' or 'Open the inventory'. Agents autonomously execute tasks and report clear pass/fail results.
- Automatic Bug Reporting: After each run, AI analysts generate detailed reports surfacing crashes, missing assets, broken menus, softlocks, and performance drops — each with descriptions, visuals, and severity scores.
- CI Pipeline Integration: Trigger automated test runs directly from the dashboard or hook them into your existing CI pipeline so every new build is tested automatically without manual intervention.
- Full Session Visibility: Agents capture video recordings, logs, and performance data during every test session, giving teams complete insight into behavior and regressions across builds.
Use Cases
- Automating regression testing after each new game build to catch bugs before they reach players
- Running exploratory QA on mobile games without requiring engineering resources or SDK integration
- Detecting visual glitches, missing assets, and UI bugs in match-3, narrative, or card games at scale
- Integrating automated game testing into CI/CD pipelines for continuous quality assurance throughout development
- Generating detailed, prioritized bug reports with video evidence to help QA teams focus on the highest-severity issues
Pros
- No Engineering Dependency: QA teams can set up and run automated tests entirely on their own — no SDK integration or code changes are ever needed, keeping development momentum unblocked.
- Plain Language Test Authoring: Tests are written in natural language, making it accessible to non-technical QA professionals without requiring scripting or programming knowledge.
- Detailed, Actionable Reports: Automatically generated bug reports include severity scores, video evidence, and descriptions that help teams prioritize and fix issues faster.
- CI/CD Ready: Seamlessly plugs into existing CI pipelines so continuous quality checks are built into the release workflow with minimal setup.
Cons
- Limited Platform Support: Currently supports Android and desktop platforms only; iOS is in development and console support is still being expanded, limiting use for multi-platform studios.
- Not Suited for Fast-Paced Gameplay: Very fast-paced or timing-critical gameplay isn't fully supported yet, meaning action-heavy or reflex-based games may require additional human testing.
- Requires Custom Model Training Per Game: Each new game benefits from a custom-trained visual model to recognize its unique UI and assets, which adds an onboarding step before testing can reach full effectiveness.
Frequently Asked Questions
Does modl.ai require integration with our game engine or codebase?
No. modl.ai operates as a black-box solution — it observes and interacts with your game purely through visuals using computer vision and OCR. QA teams can start testing immediately without any SDK, plugin, or code hook.
What platforms does modl.ai support?
modl.ai currently supports Android and desktop platforms. iOS support is in active development, and console and additional PC game workflows are being expanded.
What types of games work best with modl.ai?
modl.ai excels at testing mobile games and titles with structured interactions or clear UI elements — such as match, narrative, card, or turn-based games. Very fast-paced or timing-critical gameplay is not fully supported yet.
How do I define test cases in modl.ai?
You write test instructions in plain language, such as 'Complete the tutorial' or 'Reach level 5'. You can provide step-by-step instructions with expected outcomes or assign open-ended exploratory tasks. No scripting is required.
Can modl.ai be integrated into a CI/CD pipeline?
Yes. Test runs can be triggered automatically as part of your CI pipeline, ensuring every new build is tested without manual intervention and results are available for review immediately after each run.
