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‑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 player in the gaming sector, was founded in 2018 and secured $1.7 million in seed financing two years later. While the startup has already experimented with AI‑powered QA tools, earlier versions required developers to hand over full game builds or even source code, creating friction and limiting scalability.
According to Modl.ai founder Christoffer Holmgard, that approach proved cumbersome and "became difficult over time." The new solution, however, eliminates the need for deep integration. Holmgard explains that the system treats the game exactly as a human tester would receive it – a compiled build delivered to a device that mirrors the end‑user environment. The AI runs the game, watches the screen, simulates input, and logs every action.
A second AI layer then reviews the generated data, flags anomalies, and escalates potential bugs to a human reviewer for final validation. The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, and other relevant materials, then converse with the AI using natural‑language prompts. This dialogue helps the system develop a contextual understanding of the game's mechanics, narrative, and visual style.
Holmgard notes that the interaction is entirely conversational: "All of this is in natural language… we just chat with the system about our game, but we’re also adding images or design docs." Once the AI has a solid grasp of the title, developers specify the testing objectives. These can range from traditional test cases—such as verifying UI elements or checking level loading times—to novel scenarios tailored to the strengths of the AI, like exhaustive combinatorial input testing. Holmgard stresses that human insight remains essential at this stage: "You need expertise to shape the prompts and language so the AI does the right things." During a test run, the system records video snippets, log files, and textual descriptions of any irregularities it encounters. This evidence bundle is handed over to a senior QA specialist, who reviews the findings and decides on the appropriate course of action.
Holmgard points out a classic challenge: distinguishing genuine bugs from intentional features. Continuous feedback from human testers helps the AI refine its judgment, gradually learning which behaviors constitute defects and which are designed gameplay elements. Side sees this partnership 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. He explains that this shift can raise salaries, open new career trajectories, and even repatriate jobs to regions where low‑cost testing has previously migrated. "Instead of a linear path from tester to senior tester, people can become AI engineers," McBride says, adding that the move could bring employment back to countries that have lost testing roles due to price pressures. Beyond career development, McBride argues that AI can make QA work more rewarding.
Repetitive, low‑level tasks—such as smoke testing each new build—are prime candidates for automation. By offloading these chores to the AI, human testers can focus on strategic analysis, creative test design, and deeper gameplay evaluation. "The tool reduces the burden of repetitive tests, letting our people think more strategically and grow their careers," he notes.
Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes this sentiment, emphasizing that many current tests are monotonous and could be performed more efficiently by machines. She suggests that freeing testers from rote procedures enables them to concentrate on higher‑order activities like assessing game feel, balance, and emergent player behavior—areas where human intuition still reigns supreme. Both executives are clear that AI will not replace QA professionals; rather, it will augment them.
McBride stresses that testing costs remain low in certain regions, making a full AI takeover impractical. Instead, the technology serves as a force multiplier, extending the capabilities of existing staff and extending their tenure within the company. He describes AI as a "sliding scale" where "there will always be a human in the loop," reflecting Side’s core ethos. When asked about the types of games the system can handle today, COO Oliver Deslandes admits that the current focus is on simpler titles—puzzle games, card games, and other experiences with repeatable mechanics.
Fast‑paced, graphically intensive 3D open‑world or fighting games remain farther down the roadmap. Deslandes adds that early adopters will likely be studios willing to experiment and pilot the technology, accepting that initial results may be modest but offering valuable insight into the AI’s evolution. In summary, the Side‑Modl.ai collaboration aims to create a hybrid QA model where AI handles data‑intensive, repetitive testing while seasoned humans interpret results, provide nuanced feedback, and guide the system’s learning. By merging Side’s operational depth with Modl.ai’s AI expertise, the partnership aspires to deliver faster, more accurate bug detection, elevate the skill set of QA teams, and ultimately reshape the economics of game testing across the globe.