Side, a seasoned services provider, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence company Modl.ai. The agreement sets the stage for a joint venture to develop an AI‑powered quality‑assurance (QA) platform that blends the strengths of both organisations. Side, formerly known as PTW, has long been entrenched in the services sector, delivering testing and support across a range of industries.
Modl.ai, by contrast, is a relatively new player in the gaming arena. Founded in 2018, the startup secured $1.7 million in seed financing two years later and has since been building AI tools aimed at automating aspects of game testing. "The MOU essentially fuses Side’s deep QA expertise with Modl.ai’s cutting‑edge AI technology into a single, differentiated solution," explains Martin McBride, Side’s chief commercial officer.
"One of the holy‑grail aspirations for the games industry today is to harness AI to augment QA services in a meaningful way. No one has cracked that problem yet, but we believe we are on the brink of a breakthrough." Earlier AI‑enabled QA approaches, including Modl.ai’s initial offering, required developers to ship entire builds or even share source code with the testing vendor. That model relied on an integration plugin and SDK, creating friction and, as founder Christoffer Holmgard admits, becoming increasingly unwieldy over time. The new system eliminates the need for any internal access.
"We treat the product exactly as you would hand it to a human tester—or ultimately to the consumer," Holmgard says. "You run the game on a device that mirrors the target hardware. Our AI watches the screen, mimics key inputs, logs every observable event, and then hands the resulting data set to a second AI layer that flags potential bugs, escalates them for human review, and asks whether corrective action is warranted." The first phase of the workflow is what Modl.ai calls building "game knowledge." Developers upload assets, design documents, screenshots, or even converse with the AI in natural language to teach it the fundamentals of their title. "All interactions are plain English," Holmgard notes.
"We can chat about game mechanics, feed in images, or attach design specs, and the system gradually builds a contextual model of the game." Once that knowledge base is in place, the development team defines the testing scope. This can range from conventional test cases to novel scenarios specifically crafted for AI consumption. "Human expertise is still crucial here," Holmgard emphasizes. "You need to phrase prompts and guide the AI so it performs the right actions." During a "run"—Modl.ai’s term for a testing session—the system produces bug reports enriched with textual descriptions, short video clips (the "evidence"), and comprehensive log files.
These artefacts are then passed to a human QA specialist for validation. "The specialist reviews the report, decides what to act on, and feeds that decision back into the workflow," Holmgard explains. Distinguishing a genuine bug from an intended feature remains a nuanced challenge for AI. Feedback loops from human testers help the model learn these subtleties.
"Some learning comes from the initial prompting, some from the pre‑built game knowledge, and a significant portion comes from iterative human feedback on each bug report," Holmgard says. "Over time the system becomes attuned to the design intent of the game." Side envisions a future where QA personnel evolve into AI engineers, shifting the value chain upward. "Our people can move beyond repetitive testing to become AI specialists," McBride adds. "That not only raises their salaries but also opens new career pathways and potentially brings jobs back to regions where low‑cost testing had previously migrated away." According to McBride, AI will free testers from monotonous tasks, allowing them to focus on strategic analysis and creative problem‑solving.
"Every new build triggers smoke tests, regression checks, and countless verification steps that can bog down a tester’s workflow. By automating those repetitive elements, we enable our staff to think more strategically and grow their expertise," he says. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are tedious and involve the same sequence repeated ad infinitum. An AI can handle that, freeing humans to evaluate gameplay feel, explore edge cases, and conduct more inventive testing.
That’s the core value proposition of AI in QA." Both leaders stress that AI is not a replacement for human QA but a tool that elevates the profession. "Testing costs are still low in some regions, making a full AI takeover impractical," McBride notes.
"Providing our teams with AI‑augmented toolsets extends their careers, lets them move up the value chain, and reduces the risk of losing talent to cheaper markets." Side’s philosophy treats AI as a "sliding scale"—a continuum where human oversight remains indispensable. "There will always be a human in the loop; that’s our ethos," McBride affirms. "The AI reduces tedium and human error, raising overall quality and efficiency." When asked about the types of games the solution currently supports, COO Oliver Deslandes is candid: the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—not fast‑paced 3D open‑world or fighting games. "We’re focusing on titles where the gameplay loop is predictable enough for the AI to model effectively," he says.
"Clients willing to pilot this technology with us will help shape its evolution and determine how quickly we can expand into more complex genres." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles the grunt work, human experts provide strategic direction, and both parties benefit from a more skilled, higher‑value workforce. The journey is just beginning, but early pilots promise to showcase how AI can transform game testing while preserving the essential human touch.