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 extensive 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.
Its founder, Christoffer Holmgard, explains that earlier AI‑enabled QA tools required developers to ship full game builds or even share source code so that the AI could analyse the internals. That approach proved cumbersome and, over time, created friction between developers and testing firms. The new solution abandons the need for deep integration.
Instead, developers deliver the same package they would hand to a human tester—or ultimately to a consumer—on a device that mirrors the target platform. The AI then "plays" the game by watching the screen, simulating inputs, and recording every observable event. A second AI layer reviews this data, flags anomalies, and escalates potential bugs to a human reviewer for final validation. The process begins with what Modl.ai calls "building game knowledge." Teams upload assets, design documents, screenshots, and other relevant files, and they converse with the system in plain English.
This dialogue helps the AI construct a semantic model of the game’s mechanics, narrative, and visual language. Once that foundation is in place, developers specify the test objectives—ranging from classic regression cases to novel scenarios that are especially suited to AI‑driven exploration. Holmgard stresses that human input remains essential during the prompting stage. "You need expertise to shape the prompts and guide the AI so it performs the right actions," he says.
After the AI runs a "session"—the term Modl.ai uses for a test execution—it generates a comprehensive bug report. The report includes textual descriptions, short video clips that serve as evidence, and full log files.
These artifacts are handed to a seasoned QA specialist, who decides whether each issue warrants further action. One of the persistent challenges in game QA is distinguishing a genuine bug from an intentional feature. Holmgard notes that continuous feedback from human testers helps the AI refine its judgment.
Over time, the system learns to recognise subtle design nuances, reducing false positives and improving overall accuracy. Side sees the partnership as a pathway for its workforce to evolve into "AI engineers." Rather than remaining confined to repetitive test execution, staff can upskill, command higher salaries, and pursue new career trajectories.
Martin McBride, Side’s chief commercial officer, explains that this shift could also repatriate jobs to regions where low‑cost testing has previously migrated away. By moving employees up the value chain, Side hopes to retain talent longer and create more rewarding roles. McBride adds that AI will relieve testers of monotonous tasks such as smoke testing each new build.
"When the AI handles the routine checks, our people can focus on strategic analysis, gameplay assessment, and creative testing," he says. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that eliminating repetitive flows frees humans to evaluate the fun factor and explore innovative test scenarios.
Both executives are clear that AI is not a replacement for human QA but a complementary tool. The price of manual testing in certain low‑cost regions remains so low that a full AI takeover is impractical. Instead, the technology amplifies human capability, reduces error rates, and streamlines workflows.
Side’s chief operating officer, Oliver Deslandes, clarifies the current scope of the technology. At present, the AI performs best on simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than on fast‑paced 3D open‑world or fighting games. The company is seeking early adopters willing to pilot the system, acknowledging that results will vary based on project complexity and client expectations. In summary, the Side‑Modl.ai collaboration aims to create a hybrid QA model where AI handles data‑intensive, repetitive testing while seasoned humans provide the critical judgment and creative insight that only experience can supply.
The partnership reflects a broader industry trend: leveraging machine learning to augment—not supplant—human talent, thereby fostering more fulfilling careers and potentially reshaping the geographic distribution of QA work worldwide.