Side, a long‑standing services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence specialist Modl.ai. The agreement sets the stage for a joint effort to build an AI‑powered quality‑assurance (QA) platform that blends Side’s deep testing experience with Modl.ai’s cutting‑edge machine‑learning capabilities. Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed funding two years later. While the firm has already experimented with AI‑enabled QA tools, those earlier versions required developers to hand over entire game builds or even source code, creating friction and limiting scalability.
According to Modl.ai founder Christoffer Holmgard, the new approach eliminates the need for any internal access to the game’s codebase. "We treat the product exactly as you would hand it to a QA tester or eventually to the consumer," Holmgard explains. "The game runs on a comparable device, our system watches the screen, simulates key inputs, records everything that happens, and then passes the data to a second AI that flags potential bugs. Those findings are then reviewed by a human specialist who decides what action to take." The workflow begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, or even short video clips, and converse with the AI in plain language.
This natural‑language interaction helps the system form a contextual model of the game’s mechanics, art style, and narrative elements. Once the AI has a baseline understanding, developers define the testing scope. This can range from classic regression test cases to novel scenarios that are specifically tailored for AI interpretation.
Holmgard stresses that human expertise is still required at this stage to craft effective prompts and guide the AI toward the right objectives. During a test run—referred to by Modl.ai as a "session"—the system generates comprehensive bug reports. These reports include textual descriptions, short video snippets that serve as evidence, and full logs of the simulated gameplay.
The output is then handed to a seasoned QA analyst who validates the findings, categorises them as genuine bugs or intentional features, and determines the appropriate remediation steps. One of the persistent challenges in automated testing is distinguishing a defect from a deliberately designed feature. Holmgard notes that feedback loops from human testers are crucial for teaching the AI these subtle distinctions. Over time, as testers confirm or reject AI‑identified issues, the model becomes more attuned to the nuances of game design.
Side sees this partnership as a pathway for its QA staff to evolve into AI engineers. "Instead of a linear career that ends at senior tester, our people can transition into roles that involve AI model training, prompt engineering, and data analysis," says Martin McBride, Side’s chief commercial officer. He adds that this upskilling can raise salaries, open new career trajectories, and potentially bring jobs back to regions where low‑cost testing labor has previously migrated away.
McBride also highlights the impact on day‑to‑day work. Repetitive smoke tests and routine verifications consume a large portion of a tester’s time, leaving little room for strategic thinking. By automating these monotonous tasks, the AI frees QA professionals to focus on higher‑order analysis, gameplay evaluation, and creative testing approaches, ultimately making their roles more fulfilling. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are mind‑numbing and can be run endlessly by a machine.
That frees humans to assess the fun factor, explore edge cases, and apply creative problem‑solving—exactly the value AI brings to QA." Both leaders are careful to stress that AI will not replace QA workers but will instead elevate them. McBride notes that the cost of testing in certain low‑wage markets remains so low that a full AI takeover is neither feasible nor desirable. Instead, the technology acts as a force multiplier, allowing seasoned testers to extend their careers and contribute at a higher strategic level. Side’s philosophy can be summed up as a "sliding scale" of automation: the more routine the task, the more AI handles it; the more complex and judgment‑heavy the task, the more human involvement remains.
"There will always be a human in the loop—that’s our ethos," McBride affirms. By removing repetitive drudgery and reducing human error, the combined solution aims to raise overall QA quality. When asked about the types of games the system currently supports, COO Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, 3D open‑world or fighting games present a greater challenge and will be tackled in later development phases.
"We’re looking for partners willing to pilot this journey with us," Deslandes says, noting that early adopters may have demanding expectations around cost savings and rapid results. In summary, the Side‑Modl.ai collaboration seeks to blend human expertise with AI efficiency, creating a QA ecosystem where testers become AI‑savvy engineers, repetitive work is automated, and game developers receive faster, more accurate bug detection.
The six‑month MOU marks the first step in a longer‑term vision of reshaping quality assurance across the gaming industry.