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 agreement sets the stage for a joint effort to build an AI‑driven 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. While the company has already experimented with AI‑enabled QA tools, earlier versions required developers to hand over entire game builds—or even source code—so that the AI could analyse the product internally.
This "integration‑based" approach, which relied on plugins and SDKs, proved cumbersome and created friction for development teams, according to Modl.ai founder Christoffer Holmgard. Holmgard explains that the forthcoming system eliminates the need for any deep‑level access.
"We treat the product exactly as you would hand it to a QA tester or eventually to the consumer," he says. "You load it onto a comparable device, our solution runs the game by watching the screen, simulating key inputs, and generating a comprehensive data set of every in‑game event. A second AI layer then reviews that data, flags potential bugs, and escalates them to a human reviewer for final validation." The process begins with what Modl.ai calls building "game knowledge." Developers upload assets—such as sprites, level designs, or design documents—and converse with the AI in plain language. "All interactions are in natural language," Holmgard notes.
"We can ingest images, design docs, or any other material you provide, allowing the AI to form a contextual understanding of your title." Once the AI has a baseline grasp, developers specify the testing scope. This can range from traditional test cases to novel scenarios that are particularly suited to AI‑driven exploration.
Holmgard stresses that human insight remains essential at this stage: "You need expertise to craft prompts and guide the AI so it performs the right actions." During a "run"—Modl.ai’s term for a testing session—the system records textual logs, video snippets (referred to as "evidence"), and other telemetry. These outputs are handed to a human QA specialist who reviews the findings and decides the next steps for the production pipeline.
Holmgard points out a persistent challenge for AI: distinguishing genuine bugs from intentional features. "Feedback from QA testers helps the AI learn these nuances over time," he says. "Prompt engineering, pre‑built game knowledge, and continuous human feedback all contribute to refining the model’s accuracy." Side envisions this collaboration reshaping the career trajectory of its QA workforce. Chief Commercial Officer Martin McBride explains that, rather than remaining confined to repetitive testing roles, staff can evolve into AI engineers.
"When our people move up the value chain, they command higher salaries, open new career paths, and we can even bring jobs back to regions where low‑cost testing has previously migrated away," he says. By automating monotonous smoke tests and routine verifications, the AI frees testers to focus on strategic analysis, creative problem‑solving, and higher‑level quality assessment. Modl.ai’s Chief Product Officer Benedikte Mikkelsen echoes this sentiment, noting that many current tests are overly tedious for humans. "You don’t need a person to repeat the same flow countless times; they could instead evaluate gameplay feel or conduct exploratory testing, which adds far more value," she asserts.
Both leaders are clear that AI will not replace QA professionals but will augment them. McBride emphasizes that the price point for manual testing in certain low‑cost regions remains so low that a complete AI takeover is unrealistic. Instead, the technology acts as a force multiplier, extending the longevity and relevance of QA talent worldwide.
The partnership’s philosophy is summed up by McBride’s description of AI as a "sliding scale" with a constant human in the loop. "That’s our ethos," he 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 solution currently supports, Side’s Chief Operating Officer Oliver Deslandes notes that the system is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games.
"We’re focusing on titles where the AI can demonstrate clear value now, while we continue to refine the technology for more complex genres," Deslandes explains. He adds that early adopters willing to pilot the solution will help shape its evolution and determine how quickly it can scale to more demanding projects. In summary, the Side‑Modl.ai MOU represents a strategic blend of human expertise and artificial intelligence aimed at modernising game QA. By reducing repetitive workload, creating new technical career paths, and maintaining a human‑centric oversight model, the collaboration seeks to deliver higher‑quality gaming experiences while navigating the practical limitations of current AI capabilities.