Side, a seasoned services provider, has entered into a six‑month memorandum of understanding with Danish artificial‑intelligence specialist Modl.ai to co‑develop an AI‑driven quality‑assurance (QA) platform. The agreement binds the two companies to collaborate on a solution that blends Side’s deep QA expertise with Modl.ai’s cutting‑edge AI technology, aiming to create a more efficient, human‑augmented testing process for video games. Side, formerly known as PTW, has been a mainstay in the outsourced services arena for many years. Modl.ai, in contrast, is a relatively new player in the gaming sector.
Founded in 2018, the Copenhagen‑based startup secured $1.7 million in seed capital in 2020 and has since focused on applying machine‑learning techniques to the challenges of game testing. "The MOU essentially fuses the strongest assets of Side with the most advanced capabilities of Modl.ai into a single, differentiated offering," explains Martin McBride, Side’s chief commercial officer.
"One of the holy‑grail aspirations for the games industry today is to leverage AI to augment QA services in a way that truly adds value. Nobody has cracked that problem yet, and we believe we are on the cusp of doing so." Earlier AI‑enabled QA tools, including Modl.ai’s first generation, required developers to hand over complete game builds or even source code so the AI could run deep analyses. Those solutions relied on integration plugins and SDKs, creating friction and, as founder Christoffer Holmgard admits, becoming increasingly unwieldy over time.
The new approach eliminates the need for any internal access. "We treat the product exactly as you would hand it to a QA tester—or eventually to the consumer," Holmgard says. "You load it onto a device that mirrors the target platform, and our system plays the game by watching the screen, simulating inputs, and logging every event. A second AI layer then reviews that data, flags potential bugs, and escalates them to a human for final validation." The workflow begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, and other relevant material, then converse with the AI in natural language to teach it the fundamentals of their title.
"Everything is done via plain conversation," Holmgard notes. "We can supplement the chat with images, PDFs, or any other documentation that helps the AI form a mental model of the game." 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 level loading—to novel scenarios that are specially crafted to exploit the AI’s strengths. "At this stage you still need human expertise to phrase prompts correctly and guide the AI toward the right actions," Holmgard adds. During a test run, the system records textual logs, video snippets (referred to as "evidence"), and comprehensive telemetry.
After the session concludes, this package is handed to a seasoned QA specialist who reviews the findings and decides how to act on them. "The human reviewer determines whether a flagged issue is truly a bug, a feature, or an acceptable quirk," Holmgard explains. "Their feedback is fed back into the model, allowing it to refine its judgment over time." One of the most challenging aspects for AI is distinguishing intentional design choices from unintended defects.
Continuous input from QA testers helps the algorithm learn these subtle distinctions, gradually improving its accuracy. Side envisions a future where QA professionals evolve into AI engineers, shifting the talent pool up the value chain. "Our people can move beyond repetitive testing and become custodians of the AI system," McBride says. "That not only raises salaries and opens new career pathways, but it also makes it possible to bring jobs back to regions where low‑cost testing has been outsourced for years.
When the work becomes higher‑value, it can be performed locally again." According to McBride, automating mundane test cycles frees testers to focus on strategic analysis and creative problem‑solving, leading to more rewarding careers. "Smoke tests, regression checks, and other repetitive tasks are now handled by the AI, so our staff can spend time interpreting results, improving game balance, and contributing ideas that directly impact the player experience," he remarks. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are tedious and require a human to repeat the same flow over and over.
With AI handling those loops, testers can concentrate on higher‑order activities like assessing gameplay feel or designing innovative test scenarios. That is the core value proposition of our partnership." Both leaders stress that AI will not replace QA workers but will instead elevate them.
"Testing costs are still low in some regions, making a full AI takeover impractical," McBride observes. "Providing our teams with sophisticated tools lets them extend their careers, stay relevant, and ultimately deliver higher‑quality outcomes for our clients." Side’s philosophy treats AI as a "sliding scale"—a spectrum where human involvement never disappears.
"Our ethos is that there will always be a human in the loop," McBride affirms. "The AI removes error‑prone, repetitive work, reduces mistakes, and raises the overall standard of QA, but the final judgment always rests with a person." When asked about the types of games the solution currently supports, COO Oliver Deslandes clarifies 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 are still a ways off; we need to focus on projects where the AI can demonstrate clear value today," he says. "Our early adopters are those willing to experiment and pilot the system, accepting that the technology will evolve as we gather more data." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA model where AI handles the heavy lifting of data collection and preliminary bug detection, while seasoned human testers provide the nuanced insight needed to distinguish genuine defects from intentional design.
By upskilling their workforce and embracing a sliding‑scale AI approach, both companies hope to set a new standard for efficiency, accuracy, and career development in game quality assurance.