Side, a seasoned services provider, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai. The partnership aims to co‑develop an AI‑driven quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities. Side, previously known as PTW, has long been a fixture in the outsourcing and testing arena.
Modl.ai, by contrast, is a relatively new player in the gaming sector. Founded in 2018, the startup secured $1.7 million in seed capital two years later and has since focused on applying AI to the notoriously labor‑intensive QA workflow. “By joining forces we’re marrying the strongest assets of each organization into a truly differentiated offering,” 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 services in a meaningful way.
No one has nailed a robust solution yet—until now.” Earlier attempts at AI‑enabled QA, including Modl.ai’s own previous tools, required developers to hand over full builds or even source code so that the AI could analyse the game internally. This “integration‑based” approach, which relied on plugins and SDKs, proved cumbersome and created friction for developers, as noted by Modl.ai founder Christoffer Holmgard. Holmgard says the new system 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. You load it onto a device that mirrors the target hardware, and our solution runs the game, watches the screen, simulates key actions, and records a comprehensive data set. A second AI layer then reviews that data, flags potential bugs, and escalates items that need human judgment.” The first phase of the workflow is what Modl.ai calls building “game knowledge.” Developers upload assets, design documents, screenshots, or even short video clips, and converse with the AI in plain language.
“All interactions are natural‑language based,” Holmgard notes. “You can chat about mechanics, share images, or feed in design specs, and the AI constructs an internal 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 collision detection—to novel scenarios that play to the strengths of the AI system. “Human expertise is still crucial at this stage,” Holmgard adds.
“You need to craft prompts and guide the AI so it performs the right actions.” During a test run, the system produces bug reports that include textual descriptions, short video snippets (the “evidence”), and full log files. These artifacts are handed over to a human QA specialist for review. “The specialist decides what to do with each report—whether to open a ticket, mark it as a known issue, or close it as a non‑bug,” Holmgard explains. Distinguishing a genuine bug from an intended feature remains a subtle challenge for AI.
Holmgard says feedback loops from human testers help the model learn these nuances. “Prompt engineering, pre‑built game knowledge, and ongoing tester feedback all contribute to refining the AI’s judgment over time.” Side envisions a future where QA professionals transition into AI‑engineer roles, thereby moving up the value chain.
McBride elaborates: “Instead of a linear career that ends at senior tester, our people can become AI engineers, commanding higher salaries and opening new career pathways. This shift also lets us bring jobs back to regions where they were previously outsourced because low‑cost testing made it uneconomical to keep them locally.” According to McBride, AI will free testers from repetitive, low‑value tasks, allowing them to focus on strategic analysis and creative problem‑solving. “Smoke tests and routine verifications consume a lot of time. By automating those, our staff can think more strategically, conduct deeper analysis, and ultimately enjoy a more fulfilling career.” Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “Many current tests are mind‑numbingly repetitive.
You don’t need a human to click the same sequence over and over. With AI handling those flows, testers can concentrate on assessing gameplay feel, balance, and innovative testing approaches—adding real value.” Both executives stress that AI is not a replacement for human QA but a tool that elevates the workforce. McBride notes that testing costs in certain low‑wage countries are so low that a full AI takeover isn’t feasible yet. “Providing our people with these AI tools extends their careers and lets us retain talent longer,” he says.
Side’s philosophy frames AI as a “sliding scale” where the degree of automation can be adjusted, but a human will always remain in the loop. “That’s our ethos,” McBride affirms.
“The tool lets us capture the best of AI—speed, consistency, error reduction—while still relying on human insight to catch the edge cases that machines miss.” When asked about the types of games the solution can handle today, COO Oliver Deslandes admits 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’re focusing on titles where the AI can demonstrate value now, and we’re looking for partners willing to pilot the approach.” Deslandes concludes that the journey is just beginning. “If you’re willing to experiment with us now, you’ll see how the technology evolves and where it can take QA in the coming years.”