Side, a veteran services organization 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‑enhanced quality‑assurance (QA) platform that blends Side’s extensive testing expertise with Modl.ai’s cutting‑edge machine‑learning technology. Modl.ai, a relatively new entrant to the gaming industry, was founded in 2018 and secured $1.7 million in seed capital two years later. While the company has already experimented with AI‑powered QA tools, earlier versions required developers to ship complete builds or even share source code so the system could analyse the game from the inside out.
This “integration‑based” approach, which relied on plugins and software development kits, proved cumbersome and created friction for developers, according to founder Christoffer Holmgard. The new solution, Holmgard explains, eliminates the need for any internal access. "We treat the product exactly as you would hand it to a QA tester or eventually to a consumer," he says.
"You load the game onto a device that is either identical to or very similar to the target hardware. Our AI then runs the game, watches the screen, simulates key inputs, records every event, and passes the resulting data set to a second AI layer that flags potential bugs.
Those findings are then presented to a human reviewer who decides whether any action is required." The process begins with what Modl.ai calls "building game knowledge." Developers upload assets—such as artwork, design documents, and other media—and converse with the AI in natural language to teach it the fundamentals of the title. "All interaction is plain English," Holmgard notes.
"We can chat about the game, attach images, feed design docs, and the system gradually builds an internal model of how the game works." Once the AI has a basic understanding, the development team defines the test objectives. These can range from traditional test cases to novel scenarios that are specifically crafted to leverage the AI’s strengths.
Holmgard stresses that human expertise remains crucial at this stage: "You need skilled people to shape the prompts and guide the AI so it performs the right actions." During a test run—referred to by Modl.ai as a "session"—the system records textual logs, short video snippets (the "evidence"), and a full suite of telemetry data. This package is handed off to a human QA specialist, who reviews the findings, validates the bugs, and determines the next steps in the production pipeline. Holmgard points out a persistent challenge for AI: distinguishing a genuine defect from an intentional game feature. He explains that continuous feedback from human testers helps the AI refine its judgment over time.
"The model learns from prompts, from the pre‑built game knowledge, and from the ongoing feedback loop where testers label each report as a bug, a non‑bug, or a feature with an explanation. This iterative training gradually tunes the system’s accuracy." Side envisions a broader transformation of the QA workforce. Chief Commercial Officer Martin McBride suggests that, as AI takes over repetitive, low‑value tasks, QA professionals can evolve into "AI engineers"—roles that command higher salaries and offer clearer career progression.
This shift could also reverse the off‑shoring trend that has seen many testing jobs migrate to low‑cost regions. By moving staff up the value chain, Side hopes to bring talent back to markets where testing jobs have historically disappeared. McBride adds that the AI‑driven tool promises a more satisfying day‑to‑day experience for testers. "Repetitive smoke tests and routine verifications often bog down analysts, preventing them from thinking strategically," he says.
"When the AI automates those chores, testers can focus on deeper analysis, creative testing, and gameplay assessment, which makes their work more engaging and career‑enriching." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that many current QA tasks are monotonous and could be better performed by machines. "If a human no longer has to repeat the same flow thousands of times, they can concentrate on higher‑order activities like evaluating game feel or designing novel test scenarios. That’s the core value proposition of AI in QA," she explains.
Both executives are clear that AI will never fully replace human QA staff. McBride emphasizes that the price point for manual testing in certain regions remains so low that a complete AI takeover is impractical. Instead, the technology acts as a force multiplier, giving testers new tools to extend their careers and increase productivity.
He describes AI as a "sliding scale"—a spectrum where human involvement is always present, albeit in a more strategic capacity. "Our ethos is that there will always be a human in the loop," he says. "The AI reduces tedious work and human error, allowing the overall process to operate at a higher level of quality." When asked about the types of games the platform currently supports, Chief Operating Officer Oliver Deslandes admits that the solution is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics.
Fast‑paced 3D open‑world adventures or fighting games remain out of scope for now. "We’re focusing on titles where the gameplay loop is predictable enough for the AI to model effectively," Deslandes explains. "Clients who are willing to pilot the technology with us can help shape its evolution, but we recognize that more demanding genres will require additional development." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles high‑volume, low‑complexity testing while seasoned human experts oversee, validate, and enrich the process. By doing so, they hope to raise the overall standard of game quality, provide more rewarding career paths for testers, and lay the groundwork for future expansion into more complex gaming experiences as the technology matures.