Side, a long‑standing services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai. The agreement sets the stage for a joint effort to build an AI‑driven quality‑assurance (QA) platform that blends Side’s extensive testing operations with Modl.ai’s cutting‑edge machine‑learning capabilities. 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 via a dedicated plugin and SDK. That approach proved cumbersome and, according to founder Christoffer Holmgard, “became difficult over time” because it introduced too much friction between development teams and testing services.
The new solution under the MOU eliminates the need for any internal access to the game’s codebase. Holmgard explains that the process treats the product exactly as a QA tester—or eventually the consumer—would receive it. The game is loaded onto a device that mirrors the target platform, and the AI watches the screen, simulates inputs, records every on‑screen event, and then hands the resulting data set to a second AI layer that flags anomalies, generates bug reports, and escalates items that merit human review.
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 in plain language to teach it the fundamentals of the title. Holmgard notes that this dialogue can include images and textual descriptions, allowing the system to form a contextual model of the game without ever seeing the underlying code. Once the AI has a baseline understanding, developers specify the testing objectives. These can range from traditional test cases—such as verifying UI elements or checking level progression—to novel scenarios that are particularly well‑suited to an AI‑driven approach.
Holmgard stresses that human expertise is still required at this stage to craft effective prompts and guide the AI toward the desired outcomes. During a test run, the system records logs, captures short video clips as evidence, and compiles a comprehensive bug report.
The report is then passed to a human QA specialist, who reviews the findings, decides on the appropriate action, and provides feedback to the AI. This feedback loop helps the model differentiate between genuine defects and intentional game features, a distinction that can be subtle even for seasoned testers.
Holmgard explains that the AI learns from three sources: the initial prompting, the pre‑built game knowledge, and the ongoing feedback from human reviewers. Over time, the model becomes attuned to the nuances of the particular game, reducing false positives and improving detection accuracy. Side envisions a broader shift in the industry as a result of this partnership.
Chief Commercial Officer Martin McBride sees the technology as a pathway for QA staff to evolve into AI engineers, thereby moving “up the value chain.” He argues that this transition can raise salaries, open new career trajectories, and even bring jobs back to regions where they have previously disappeared due to cost pressures. By automating repetitive, low‑skill tasks, testers can focus on strategic analysis, creative problem‑solving, and higher‑level quality work. McBride adds that the AI tool already eases the burden of routine smoke tests and verification steps that must be repeated for every new build.
By offloading these chores to the machine, human testers gain the bandwidth to think more strategically, conduct deeper gameplay analysis, and contribute ideas that improve the overall player experience. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that many current tests are “tedious” and involve running the same flow countless times. She argues that freeing testers from such monotony allows them to concentrate on more valuable activities, such as assessing game balance or inventing novel testing scenarios.
Both executives are clear that AI will not replace QA professionals but will augment them. McBride points out that testing costs remain low in certain regions, making a full AI takeover impractical. Instead, providing the workforce with sophisticated AI tools extends their careers, enhances job satisfaction, and keeps talent within the company longer.
The partnership’s philosophy can be summed up by Side’s description of AI as a “sliding scale.” There will always be a human in the loop—a principle that underpins the company’s ethos. By combining AI’s speed and consistency with human judgment, Side believes it can reduce errors, increase efficiency, and deliver higher‑quality games.
When asked about the types of games the system can handle today, Chief Operating Officer Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, graphically intensive 3D open‑world or fighting games remain a future goal.
Deslandes emphasizes that early adopters should be willing to experiment and accept that the solution will evolve as the partnership gathers data and refines its models. In summary, the Side‑Modl.ai MOU represents a strategic attempt to fuse human expertise with AI efficiency in game QA. By creating a workflow that starts with natural‑language interaction, progresses through automated gameplay observation, and culminates in human‑validated bug reporting, the collaboration aims to set a new standard for how games are tested, how QA talent is developed, and how value is created across the global gaming ecosystem.