Side, a veteran 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 create an AI‑enhanced quality‑assurance (QA) platform that blends Side’s deep industry 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 company has already experimented with AI‑driven QA tools, earlier versions required developers to ship full builds or even share source code via plugins and SDKs. This approach generated significant friction, as developers were reluctant to expose internal assets and the integration process proved cumbersome over time, according to founder Christoffer Holmgard.
The new solution, Holmgard explains, eliminates the need for any direct access to a game’s internal files. "We treat the product exactly as you would hand it to a QA tester or eventually to the consumer," he says.
"You load it onto a comparable device, and our system plays the game by watching the screen, simulating key inputs, and recording every observable event. That data set is then handed to a second AI layer that flags potential bugs, escalates them to a human reviewer, and asks whether corrective action is required." The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets—such as sprites, level designs, or design documents—and converse with the AI in natural language. "We simply chat with the system about our game, supplementing the dialogue with images or design docs," Holmgard notes. This conversational interface helps the AI construct a semantic model of the game’s mechanics, narrative, and visual style.
Once the AI has a baseline understanding, developers specify the testing objectives. These can range from traditional test cases (e.g., verifying that a menu opens correctly) to more exploratory scenarios that are "suitable for the AI system." Holmgard stresses that human expertise is still crucial at this stage: "You need to shape the prompts and language so the AI knows exactly what to look for." During a test run, the system generates bug reports that include textual descriptions, short video clips as evidence, and comprehensive log data. These artifacts are routed to a human QA specialist, who reviews them and decides the next steps for the production pipeline.
"The AI surfaces the data, but the final judgment remains with an experienced tester," Holmgard emphasizes. One of the persistent challenges in automated testing is distinguishing a genuine defect from an intended feature. Holmgard explains that feedback loops from human testers help the AI refine this judgment.
"Prompt engineering, the pre‑built game knowledge, and ongoing tester feedback all contribute to teaching the model what constitutes a bug versus a design choice. Over time, the system becomes attuned to those nuances." Side envisions this technology reshaping the career trajectory of its QA workforce.
Chief Commercial Officer Martin McBride says the partnership enables staff to transition from routine testing roles to "AI engineers" who design prompts, interpret AI outputs, and fine‑tune models. This shift, he argues, can lift salaries, open new career paths, and even repatriate jobs to regions where low‑cost testing has previously driven employment away. McBride also highlights the impact on job satisfaction. By automating repetitive smoke tests and other monotonous checks, QA professionals can focus on strategic analysis, creative testing, and deeper gameplay evaluation.
"When the AI handles the grunt work, our people can think more strategically and grow their expertise," he states. Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes this sentiment: "Many current tests are tedious and involve repeating the same flow thousands of times. An AI can take over those cycles, freeing humans to assess gameplay feel, balance, and innovative testing approaches.
That’s the real value proposition." Both leaders are clear that AI will not replace QA staff but will augment them, moving people "up the value chain." McBride points out that testing costs remain low in certain countries, making a full AI takeover economically impractical. Instead, providing advanced toolsets empowers employees to extend their careers and remain competitive. Side’s philosophy treats AI as a "sliding scale"—a spectrum where human involvement gradually recedes as automation increases, but never disappears entirely. "There will always be a human in the loop; that’s our ethos," McBride affirms.
By removing tedious, error‑prone tasks, the AI raises overall quality and consistency while preserving the essential human judgment that defines good game design. When asked about the types of games the system can currently handle, Chief Operating Officer Oliver Deslandes admits 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 remain a longer‑term goal.
"We’re focusing on projects where the mechanics are predictable and the pace is moderate, allowing us to demonstrate value to early adopters," Deslandes explains. He adds that the journey is collaborative: "Clients who join us now for pilot projects will see the evolution of the tech firsthand. As we refine the platform, we’ll expand its applicability to more complex genres." In summary, the Side‑Modl.ai partnership aims to fuse human expertise with AI efficiency, creating a QA ecosystem where routine testing is automated, human testers become AI specialists, and the overall quality of game releases improves. The six‑month MOU marks the beginning of a collaborative experiment that could reshape how the gaming industry approaches quality assurance, balancing cutting‑edge technology with the irreplaceable insight of seasoned professionals.