Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai. The partnership is aimed at co‑creating an AI‑driven quality‑assurance (QA) platform that blends Side’s deep testing experience 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 funding two years later. While the company has already experimented with AI‑enabled QA tools, earlier versions required developers to hand over complete builds or even source code, creating friction and limiting scalability. According to Modl.ai founder Christoffer Holmgard, those integration‑heavy approaches proved cumbersome and eventually “became difficult over time.” The new solution eliminates the need for internal access. “We take the product exactly as you would hand it to a QA tester or eventually to a consumer,” Holmgard explains.
“You load it onto a comparable device, and our system runs the game, watches the screen, simulates key actions, records everything that happens, and then passes that data to a second AI that flags potential bugs, escalates them for human review, and asks whether they merit further action.” The process begins with what Modl.ai calls building “game knowledge.” Developers upload assets, design documents, screenshots, or any other relevant material and converse with the AI in natural language. “We simply chat with the system about our game, supplementing the dialogue with images or design docs,” Holmgard says. This conversational interface allows the AI to develop a contextual understanding of the title before any testing begins. Once the AI has a baseline comprehension, developers specify the testing objectives.
These can range from traditional test cases to novel scenarios that are particularly suited to AI‑driven exploration. Holmgard stresses that human expertise is still crucial at this stage: “You need to shape the prompts and language so the AI knows exactly what to do.” During a testing run—referred to by Modl.ai as a “session”—the system generates bug reports enriched with textual descriptions, short video snippets (the so‑called “evidence”), and comprehensive log data. These artifacts are handed over to a human QA specialist, who reviews them and decides how to incorporate the findings into the production pipeline.
One of the perennial challenges in game QA is distinguishing a genuine defect from an intentional feature. Holmgard notes that feedback loops from human testers help the AI learn these nuances.
“Some of the learning comes from the prompts, some from the pre‑built game knowledge, and a significant portion comes from ongoing human feedback on each bug report—whether it’s a bug, not a bug, or a design choice.” Over time, the AI becomes attuned to the specific design language of the studio. Side envisions a future where QA personnel evolve into AI engineers, thereby shifting the value chain upward. Chief Commercial Officer Martin McBride explains, “Instead of a career that stops at senior tester, our people can become AI engineers, earn higher salaries, and open new career pathways.” He adds that this shift could even bring QA jobs back to regions where they have previously disappeared due to low cost pressures, because the higher‑skill roles are less price‑sensitive.
McBride also argues that AI will make QA careers more fulfilling. Repetitive smoke tests and routine verifications consume a large portion of a tester’s day, limiting strategic thinking.
By automating these monotonous tasks, testers can focus on analysis, creative problem‑solving, and higher‑order quality work. “The tool reduces the burden of repetitive tests, allowing our people to think strategically and grow professionally,” he says.
Modl.ai’s Chief Product Officer Benedikte Mikkelsen echoes this sentiment, noting that many current tests are “tedious” and could be performed by a machine. “If a human no longer has to repeat the same flow over and over, they can spend time assessing gameplay or conducting more inventive testing, which is the real value proposition of AI.” Both leaders stress that AI will never fully replace QA staff. The price point for low‑cost testing in some regions remains too low to eliminate human involvement entirely. Instead, AI serves as a tool that “moves people up the value chain,” extending careers and improving retention.
Side’s philosophy treats AI as a “sliding scale”: there will always be a human in the loop, but the proportion of automation can be adjusted based on project needs. “That’s our ethos,” McBride says.
“The tool lets us harness AI’s strengths—speed, consistency, error reduction—while still relying on human judgment for the nuanced decisions that only experienced testers can make.” When asked about the types of games the system currently supports, COO Oliver Deslandes admits that the technology is best suited for simpler titles—puzzles, card games, or other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games. “We’re focusing on projects where the mechanics are predictable enough for the AI to learn effectively,” he explains.
He adds that early adopters willing to pilot the technology will help shape its evolution and expand its applicability. In summary, the Side‑Modl.ai partnership aims to blend human expertise with AI efficiency, creating a QA workflow that reduces repetitive labor, enhances career prospects for testers, and gradually extends AI capabilities to more complex gaming experiences. The six‑month MOU marks the first step in a longer journey toward AI‑augmented quality assurance that respects both technological innovation and the indispensable role of human insight.