Side, a seasoned services provider, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence specialist Modl.ai. The agreement commits the two companies to collaborate on building an AI‑driven quality‑assurance (QA) platform that blends Side’s extensive testing expertise with Modl.ai’s cutting‑edge machine‑learning technology.
Side, formerly known as PTW, has been a fixture in the services arena for many years, delivering testing, localisation and support services to game developers worldwide. Modl.ai, by contrast, is a relatively new player in the gaming sector. Founded in 2018, the startup secured $1.7 million in seed funding in 2020 and has since focused on creating AI tools that can automate parts of the QA process.
"The MOU essentially fuses the strongest assets of Side and Modl.ai into a single, differentiated solution," explains Martin McBride, Side’s chief commercial officer. "One of the holy‑grail aspirations for the games industry today is to harness AI to augment QA. No one has cracked that problem yet, but we believe we are on the brink of a breakthrough." Earlier AI‑enabled QA approaches, including Modl.ai’s own initial offering, required developers to ship full builds or even source code to the testing vendor.
Those integrations relied on plugins and SDKs, creating considerable friction and, as co‑founder Christoffer Holmgard admits, becoming "difficult over time." The new system eliminates that barrier. "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 end‑user hardware, and our solution runs the game by watching the screen, simulating key inputs, and recording everything that happens. A second AI then analyses that data, flags potential bugs, and escalates the findings to a human reviewer for validation." The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots and other relevant files, then converse with the AI in natural language to teach it the fundamentals of the title.
"All interaction is plain English," Holmgard notes. "You can chat with the system, feed it images, or attach design docs, and it gradually builds a semantic model of your 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 more exploratory scenarios that are uniquely suited to AI‑driven analysis. "Here you need human expertise to craft prompts that guide the AI correctly," Holmgard adds, emphasizing the collaborative nature of the process. During a "run" (Modl.ai’s term for a testing session), the system captures a wealth of artefacts: textual logs, video snippets that serve as evidence, and comprehensive telemetry.
These outputs are handed to a human QA specialist, who reviews the findings, decides which issues merit further action, and provides feedback that the AI can learn from. "Distinguishing a bug from an intentional feature is a subtle judgment call," Holmgard explains. "Human feedback helps the model refine its understanding over time." Side envisions this feedback loop eventually reshaping the role of QA professionals.
Rather than remaining confined to repetitive test execution, staff can evolve into "AI engineers" who design prompts, interpret AI‑generated insights, and fine‑tune models. McBride says this shift can elevate salaries, open new career pathways, and even bring jobs back to regions where low‑cost testing had previously driven work offshore. "When you move people up the value chain, you can re‑invest in economies that lost those jobs," he asserts.
The AI tool also promises a more satisfying daily experience for testers. By automating mundane smoke tests and regression checks, the technology frees humans to focus on strategic analysis, creative exploration of gameplay, and higher‑order problem solving. "Our people can spend less time clicking the same button a hundred times and more time thinking about game balance or player experience," McBride says. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes that sentiment: "There are many tedious tests that don’t need a human to repeat over and over.
If you free testers from those loops, they can concentrate on evaluating the game itself or conducting innovative testing methods. That’s the core value proposition of AI in QA." Both leaders stress that AI will never replace human QA workers entirely. The price point for low‑cost testing in certain regions remains so low that a complete AI takeover is economically unrealistic.
Instead, the technology acts as a force multiplier, allowing seasoned testers to extend their careers, take on more complex responsibilities, and stay relevant in a rapidly evolving industry. McBride describes Side’s philosophy as a "sliding scale" of AI involvement: "There will always be a human in the loop—that’s our ethos. The tool lets us capture the best of AI—speed, consistency, error reduction—while retaining human judgment for the nuanced decisions that only experienced QA specialists can make." When asked about the types of games the solution currently supports, COO Oliver Deslandes is candid. The system is best suited for simpler titles—puzzle games, card games, or any experience with repeatable mechanics and modest frame rates.
"Fast‑paced 3D open‑world adventures are still a ways off," he says. "We’re focusing on projects where the AI can demonstrate clear value now, and we’re looking for partners willing to pilot the technology and help us iterate." Deslandes concludes that the journey is just beginning. Early adopters who engage in pilot programs will see firsthand how the AI evolves, how feedback loops improve accuracy, and how the balance between automation and human insight shifts over time. "It’s a collaborative path forward," he remarks, "and the results will shape the next generation of QA for the games industry."