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 relative newcomer to the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later.
While the firm has already experimented with AI‑driven QA tools, earlier versions required developers to ship entire builds or even share source code so that the system could run its analyses. That approach proved cumbersome, created friction with development teams, and ultimately limited scalability, according to Modl.ai founder Christoffer Holmgard. The new solution eliminates the need for any internal code access. "We treat the product exactly as you would hand it to a QA tester or eventually to a consumer," Holmgard explains.
"You load it onto a device that mirrors the target hardware, and our system plays the game, watches the screen, simulates key inputs, records everything that happens, and then hands the resulting data to a second AI layer that flags potential bugs. That layer then escalates the findings to a human reviewer for validation." The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, or any other relevant material, and they converse with the AI in plain language.
The system ingests these inputs, forming a contextual understanding of the game's mechanics, narrative, and visual style. This natural‑language interaction makes the onboarding process intuitive for developers who may not be AI experts. Once the AI has a baseline grasp of the title, developers specify the testing objectives. These can range from traditional test cases—such as verifying menu navigation or level loading—to novel scenarios that are especially suited to AI‑driven exploration.
Holmgard stresses that human insight remains essential at this stage: "You need to craft prompts and guide the AI so it performs the right actions. That requires expertise in both testing methodology and prompt engineering." During a test run, the system generates comprehensive bug reports.
Each report includes textual descriptions, short video clips that serve as visual evidence, and detailed log files. These artifacts are then reviewed by a seasoned QA specialist who decides how to act on the findings. "The human in the loop validates whether an anomaly is truly a defect or an intended feature," Holmgard notes.
"Their feedback is fed back into the AI, refining its judgment over time." One of the perennial challenges in game QA is distinguishing bugs from intentional design choices. The iterative feedback loop—where human testers label outcomes as bugs, non‑bugs, or edge‑case features—helps the AI learn the subtle nuances of game design.
Over successive cycles, the model becomes better attuned to the developer’s intent, reducing false positives and improving detection accuracy. Side sees this collaboration as a pathway to elevate its workforce. Chief Commercial Officer Martin McBride envisions QA staff transitioning into AI engineers, thereby moving up the value chain. "Instead of a career that ends at senior tester, our people can become AI specialists, earn higher salaries, and open new career trajectories," he says.
This shift also has geopolitical implications: by upskilling staff, Side hopes to repatriate jobs to regions where testing had previously migrated due to lower labor costs. McBride adds that AI will free testers from repetitive, low‑skill chores, allowing them to focus on strategic analysis and creative problem‑solving. "Smoke tests, regression checks, and other routine verifications can be automated, so our team can spend more time interpreting data, shaping test strategies, and contributing to game design decisions," he explains. This, he believes, leads to more fulfilling careers and higher employee retention.
Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes the sentiment: "Many current tests are monotonous—running the same flow repeatedly offers little value to a human. By automating those, testers can concentrate on evaluating gameplay, exploring edge cases, and performing inventive testing that adds real insight. That’s the core value proposition of AI in QA." Both leaders are clear that AI is not a replacement for human testers.
The technology is intended to augment, not supplant, the workforce. McBride points out that testing costs remain low in certain regions, making a full AI takeover economically infeasible.
Instead, the AI toolkit serves as a career‑enhancing instrument, extending the longevity and relevance of QA professionals. The partnership also embraces a philosophical stance that AI operates on a "sliding scale"—the degree of automation can be adjusted, but a human will always remain in the loop. "That’s our ethos," McBride affirms. "By removing tedious, error‑prone tasks, we raise overall quality and allow our people to apply their expertise where it matters most." When asked about the types of games the system currently supports, Side’s Chief Operating Officer Oliver Deslandes is candid: the technology is best suited for simpler titles—puzzle games, card games, or other experiences with repeatable mechanics and moderate pacing.
Fast‑paced 3D open‑world or fighting games present a greater challenge and are slated for future development. "We’re targeting clients who are willing to embark on this journey with us," Deslandes says. "Some will demand immediate cost savings and rapid results; others will be more exploratory.
It’s a collaborative evolution." In summary, the Side‑Modl.ai MOU represents a strategic blend of human expertise and artificial intelligence aimed at reshaping game QA. By leveraging AI to handle repetitive testing while retaining human judgment for nuanced decisions, the partnership aspires to improve defect detection, accelerate release cycles, and create more rewarding career paths for QA professionals. The initiative is still in its early phases, but both companies are optimistic that, with pilot projects and iterative learning, the technology will expand to cover increasingly complex game genres and deliver measurable value to developers worldwide.