ManaMind

What ManaMind does

ManaMind automates quality assurance in video games with AI bots that play games, discover bugs, and generate bug reports like a human. It provides continuous testing with autonomous agents that behave like real players, tracking what bots find across every build in a command center. ManaMind augments the QA process with autonomous, continuous testing, addressing the complexity of modern games that manual testing alone cannot keep up with. The company is building vision-driven agents that automate quality assurance in video games, with a long-term vision to expand to general software testing (apps, ERPs, websites) and robotics.

Key capabilities

• Autonomous agents that behave like real players
• Command Centre for real-time reporting and tracking
• Multi-layer AI system for perception to orchestration
• Bots interact through normal player inputs without internal integration
• AI analyzes individual frames for UI elements, objects, and scene layout
• AI tracks sequences of frames for movement, animation, and cause-and-effect
• Zero-Shot Testing: operates in new titles without per-game training or scripting
• Models trained across diverse games and UI patterns for generalization
• Full regression cycles completed in hours
• High critical bug detection rate
• Parallel coverage across multiple device configurations
• Designed for confidential environments (early prototypes, pre-release builds)
• No source code access required
• Works with closed-source or externally developed titles
• Simplifies security reviews and internal approvals
• Records gameplay sessions

Use cases

• Continuous testing of video games
• Automated bug discovery
• Automated bug report generation
• Augmenting manual QA processes
• Regression testing
• Testing unreleased and sensitive game builds
• General software testing (future vision)
• Robotics (long-term vision)

AI approach

ManaMind uses a multi-layer AI system, from perception to orchestration, to create autonomous agents that test video games. These agents observe the game world and interact through normal player inputs, analyzing individual frames for UI elements, objects, and scene layout, and tracking sequences of frames to understand movement and cause-and-effect. Their models are trained across diverse games and UI patterns, enabling zero-shot testing without per-game scripting or training.

Tech named: multi-layer AI system, perception, orchestration, AI bots, vision-driven agents, zero-shot testing, models trained across diverse games and UI patterns

Industries served

• Video Games
• Software Development (future vision)
• Robotics (long-term vision)

What it says sets it apart

• Autonomous, continuous testing with AI bots
• Behaves like real players, not scripts
• Zero-Shot Testing: no scripting or training required per game
• Adapts to new layouts and interactions
• Faster testing and broader coverage compared to traditional automation
• Higher bug capture rates
• No SDKs, scripts, or engine access required for integration
• Significant reduction in QA operating costs
• Scalability by running more autonomous sessions in parallel
• Does not require access to source code, engine internals, or proprietary debugging interfaces
• Elevates human testers by handling scale and repetition

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.