The Future of Quality Assurance: How Side and Modl.ai Are Redefining the Role of AI in Gaming

In a landmark move for the games industry, services provider Side—formerly known as PTW—has entered into a strategic memorandum of understanding (MOU) with Danish artificial intelligence specialist Modl.ai. This binding six-month partnership aims to revolutionize the quality assurance (QA) landscape by integrating advanced AI automation with human expertise.

As the gaming industry faces increasing pressure to deliver polished, bug-free experiences amidst rising development costs and shrinking timelines, the collaboration between Side and Modl.ai represents a pivotal shift. Rather than viewing AI as a replacement for the human workforce, the companies are positioning the technology as a sophisticated "co-pilot," designed to handle the drudgery of repetitive testing while elevating QA specialists into more strategic, high-value roles.

The Genesis of the Partnership: A Technical Evolution

The collaboration is not merely a theoretical exercise. Modl.ai, founded in 2018 and bolstered by $1.7 million in seed funding, brings a wealth of experience in AI-driven game testing. Previously, the industry’s approach to AI-assisted QA was hampered by technical friction. Developers were often required to provide source code and integrate complex plugins or SDKs, creating a heavy administrative burden that many studios were reluctant to shoulder.

"The MOU effectively brings the best of what Side has and the best of what Modl.ai has together in a unique solution," says Martin McBride, Chief Commercial Officer at Side. "One of the ‘holy grail’ experiences for the games industry right now has been using AI technology to augment QA services. No one has really found a great solution for that until now."

The breakthrough lies in the "black-box" nature of the new system. Unlike previous iterations that required deep integration with game internals, the new solution treats the AI like a human tester. It consumes the game as a consumer would, observing the screen and simulating key inputs. By generating a comprehensive dataset of gameplay, the system allows a secondary AI layer to flag potential bugs, which are then escalated to a human expert for final verification.

How the System Works: Building ‘Game Knowledge’

The process begins with a phase known as "game knowledge" acquisition. Developers engage with the system using natural language, effectively "chatting" with the AI about their project. By uploading design documents, assets, and visual references, the developers prime the AI to understand the game’s mechanics, artistic direction, and intended behavior.

"We just chat with the system about our game, but we’re also adding in images or uploading game design documents," explains Christoffer Holmgård, founder of Modl.ai.

Once the AI is "briefed," the development team defines the test parameters. This is where the partnership emphasizes the necessity of human oversight. QA experts are required to translate high-level design goals into specific prompts for the AI agent, ensuring the system focuses on the most critical elements. Once the "run" is complete, the system outputs a detailed report containing logs, video evidence, and text-based bug descriptions. The final decision—whether to treat an anomaly as a bug or a deliberate feature—remains firmly in the hands of the human QA specialist.

Addressing the ‘Bug vs. Feature’ Dilemma

One of the most complex challenges in game QA is discerning between a technical error and a design choice. Holmgård notes that the system is designed to learn through a feedback loop. When a human tester reviews an AI-generated report and labels it as either a valid bug or a non-issue, the AI logs this data. Over time, the model becomes increasingly "attuned" to the specific nuances of a project, reducing false positives and increasing the precision of future test runs.

Implications for the Workforce: The Rise of the AI Engineer

Perhaps the most significant aspect of this partnership is the stated intent to upskill the workforce. Rather than eliminating roles, Side aims to transition its QA staff into "AI engineers."

"One of the cool parts about programs like this is that we get to move our people up the value chain," says McBride. "Instead of just doing testing—moving from junior to senior tester—they can become AI engineers. It allows our people to get paid more money, creates new career paths, and potentially allows us to bring jobs back to countries where they have historically left due to labor costs."

This shift addresses a long-standing issue in the services industry: the repetitive nature of "smoke tests" and routine verifications. By automating these tasks, testers are freed from the most tedious aspects of their jobs, allowing them to focus on gameplay analysis, creative edge cases, and strategic oversight. Benedikte Mikkelsen, Chief Product Officer at Modl.ai, underscores this: "You don’t really need a human doing the same flow over and over again. That person could be doing something much more valuable, like assessing the gameplay itself."

Technical Constraints and Future Roadmap

Despite the enthusiasm surrounding the project, the companies remain realistic about the current capabilities of the technology. Oliver Deslandes, Chief Operating Officer at Side, is transparent about the limitations of the current iteration.

"The tech right now works with more simplistic titles," Deslandes notes. "We are focusing on puzzles, card games, and titles with repeatable, slower-paced mechanics. Fast-paced, high-fidelity 3D open-world adventures are a little further down the line."

The goal is to invite clients to join this "journey." While some studios are primarily interested in immediate cost reduction, the long-term potential of the system lies in its scalability. As the AI grows more sophisticated, the scope of games it can effectively test will expand, gradually moving from simple mechanics to more complex, dynamic environments.

The Human-in-the-Loop Philosophy

The overarching ethos of the Side and Modl.ai partnership is the "human-in-the-loop" model. In an era where fears of total automation are prevalent, the companies are doubling down on the necessity of human intuition.

"There will always be a human in the loop," McBride emphasizes. "This tool allows our people to take the best of what AI can offer and reduce the burden and the margin for error. A lot of this work is tedious, and humans are prone to making mistakes in repetitive tasks. You take that out of the equation, and everything ratchets up a notch."

Conclusion: A New Standard for Quality

The six-month MOU serves as a pilot program that could set the standard for how the industry handles QA in the future. By merging Modl.ai’s cutting-edge automation with Side’s global infrastructure and deep human expertise, the partnership aims to solve the "holy grail" of QA: faster, more accurate testing that doesn’t sacrifice the creative or strategic input of human testers.

For game developers, this means the potential for higher-quality releases with fewer launch-day headaches. For QA professionals, it offers a glimpse into a more rewarding career path, one where technology acts as an amplifier of human skill rather than a replacement. As the industry watches this collaboration unfold, the message is clear: the future of game quality isn’t just about better machines—it’s about better-supported people.