Side, a seasoned 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 expertise with Modl.ai’s cutting‑edge machine‑learning technology. Modl.ai, a relatively new player in 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‑enabled QA tools, earlier versions required developers to hand over entire game builds or even source code so the system could run integration‑based tests through a plugin and SDK.
That approach generated considerable friction, as developers found it cumbersome and, over time, impractical, according to Modl.ai founder Christoffer Holmgard. The new solution abandons 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 watches the screen, mimics key actions, records every visual and data point, and then hands the resulting dataset to a second AI layer that flags anomalies, escalates them to a human reviewer, and asks whether they merit action." The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets—artwork, design documents, level files—and converse with the AI in plain language. The system ingests these inputs, creating a semantic model of the game’s mechanics, aesthetics, and intended player experience.
Holmgard notes that the interaction is entirely natural‑language based, allowing teams to supplement text with images or design PDFs to enrich the AI’s understanding. Once the AI has a solid grasp of the title, developers specify the testing objectives. These can range from classic regression test cases to novel scenarios that are especially suited to AI‑driven exploration. "Human expertise is still crucial at this stage," Holmgard says, stressing that testers must craft prompts and guide the AI’s language so it executes the right actions.
During a test run—referred to by Modl.ai as a "session"—the system captures comprehensive evidence: textual logs, video snippets that serve as visual proof, and detailed telemetry. This package is then delivered to a human QA specialist, who reviews the findings, validates whether each flagged item is truly a bug, and decides the appropriate next steps for the development pipeline. One of the perennial challenges in automated testing is distinguishing a genuine defect from an intentional feature. Holmgard points out that ongoing feedback from human testers helps the AI fine‑tune its judgments.
"Prompt engineering, pre‑built game knowledge, and iterative human feedback all converge to teach the model the subtle nuances of game design," he says. Over time, the AI becomes more attuned, reducing false positives and improving its predictive accuracy. Side sees this collaboration as a pathway to elevate its QA workforce. Instead of remaining confined to repetitive test execution, staff can transition into roles akin to AI engineers, overseeing model training, prompt design, and result interpretation.
Chief Commercial Officer Martin McBride explains that this shift moves employees up the value chain, offering higher salaries, new career trajectories, and the possibility of bringing jobs back to regions that previously lost them due to low‑cost labor pressures. McBride also argues that AI can make QA careers more rewarding.
By automating mundane smoke tests and routine verifications for each new build, testers are freed to focus on strategic analysis, gameplay assessment, and creative problem‑solving. "When the repetitive load is lifted, people can think more strategically and grow professionally," he says. Modl.ai’s Chief Product Officer Benedikte Mikkelsen echoes this sentiment, noting that many current tests are tedious loops that a human need not repeat.
"If the AI can handle the rote flows, our people can concentrate on evaluating the actual gameplay experience and conducting more inventive testing approaches," she adds, framing the AI’s value proposition as a productivity multiplier rather than a replacement. Both leaders stress that AI will never fully replace human QA talent. The price point for low‑cost testing in certain markets remains so low that a complete AI takeover is economically infeasible. Instead, the technology serves as an augmentation tool, enabling testers to extend their careers, take on higher‑impact tasks, and remain valuable contributors for longer periods.
Side’s philosophy toward AI is described as a "sliding scale"—a spectrum where the proportion of automation can be adjusted, but a human will always remain in the loop. "That’s our ethos," McBride affirms. "The tool lets us capture the best of AI—speed, consistency, error reduction—while still relying on human judgment to catch edge cases and ensure 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 greater challenges and are slated for future development. Deslandes emphasizes that early adopters should be willing to embark on a pilot journey, recognizing that the solution will evolve based on their feedback and requirements. 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 specialists provide oversight, context, and strategic insight. By doing so, the collaboration hopes to raise the overall quality of game releases, improve tester satisfaction, and open new, higher‑value roles within the QA profession.