Side, a long‑standing services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai. The agreement sets the stage for a joint effort to build an AI‑driven quality‑assurance (QA) platform that blends Side’s deep industry experience with Modl.ai’s cutting‑edge technology. Modl.ai, although a newer player in the gaming arena, has been around since 2018 and secured $1.7 million in seed financing two years after its launch. Its chief commercial officer at Side, Martin McBride, describes the partnership as a marriage of the best each company has to offer: “One of the crown‑jewel, holy‑grail experiences for the games industry right now is using AI to augment QA services.
No one has nailed a robust solution yet – we think we are close.” Earlier AI‑enabled QA tools, including Modl.ai’s own first‑generation product, required developers to ship full builds or even share source code so that the AI could analyse the game from within. This “integration‑based” approach, which relied on plugins and SDKs, proved cumbersome and eventually became a barrier to adoption, according to founder Christoffer Holmgard. The new system eliminates that friction.
Holmgard explains the revamped workflow: developers hand over the same version of the game they would give a human tester—or ultimately the consumer—running it on a comparable device. The AI then watches the screen, mimics player inputs, logs every observable event, and passes the resulting dataset to a second AI layer that flags anomalies, generates bug reports, and escalates items that need human judgment.
The first phase is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, and other relevant files, then converse with the AI in natural language to teach it the fundamentals of the title. “We just chat with the system about our game, adding images or design docs as needed,” Holmgard says. Once the AI has a baseline understanding, the team defines test objectives.
These can range from traditional test cases to novel scenarios that play to the strengths of the AI. Here, human expertise is still essential: “You need to craft prompts and guide the AI so it does the right thing,” Holmgard notes. During a test run, the system records textual logs, short video clips (referred to as “evidence”), and comprehensive telemetry.
This package is handed to a human QA specialist who reviews the findings and decides the next steps for production. Holmgard highlights a persistent challenge: distinguishing a genuine bug from an intentional feature. Feedback loops from human testers help the AI refine its judgment over time, gradually learning which signals indicate defects and which reflect intended gameplay.
Side envisions this evolution reshaping QA careers. Rather than remaining in repetitive testing roles, staff can transition to “AI engineers,” overseeing the prompts, interpreting AI output, and improving the underlying models. McBride says this shift “moves our people up the value chain,” enabling higher wages, new career trajectories, and the possibility of bringing jobs back to regions where low‑cost testing had previously driven work offshore. The AI tool also promises to make QA work more rewarding.
By automating routine smoke tests and other monotonous checks, testers can focus on strategic analysis, creative exploration, and higher‑order problem solving. “When you knock out the repetitive work, people can think more strategically and grow their careers,” McBride adds.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “There are very tedious tests that don’t need a human to repeat the same flow over and over. Those people could instead assess gameplay or perform more inventive testing.
That’s the value proposition of AI.” Both leaders stress that AI will never replace QA professionals entirely. The price of testing in certain low‑cost regions remains too low to justify a full AI takeover, and human insight is still required for nuanced decision‑making.
Instead, AI serves as a force multiplier, reducing error rates and freeing humans from drudgery. “There will always be a human in the loop—that’s our ethos,” McBride affirms, describing AI as a “sliding scale” that can be adjusted to the needs of each project. When asked about the types of games the solution currently supports, COO Oliver Deslandes admits the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world adventures or fighting games.
“We’re targeting clients willing to embark on this journey with us,” he says, noting that early adopters may have higher expectations for cost savings and rapid results. In summary, the Side‑Modl.ai partnership aims to create a hybrid QA model where AI handles the bulk of repetitive testing and data collection, while seasoned human testers interpret results, provide feedback, and continuously train the system. This collaborative approach aspires to raise the overall quality of games, improve employee satisfaction, and potentially reshape the global distribution of QA talent as AI tools become more sophisticated.