Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence specialist Modl.ai. The agreement sets the stage for a joint effort to develop an AI‑powered quality‑assurance (QA) platform that blends Side’s deep industry 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 financing two years later. Its founders have spent the past several years refining AI tools that can assist QA teams, but earlier versions required developers to ship entire game builds or even share source code with the testing vendor.
That approach proved cumbersome, created security concerns, and often stalled adoption because of the high integration friction. According to Modl.ai’s founder Christoffer Holmgard, the new solution sidesteps those obstacles entirely.
"We treat the product exactly as you would hand it to a QA tester or eventually to the consumer," he explains. "The game runs on a device that mirrors the target hardware, our system watches the screen, simulates key inputs, records every interaction, and then hands the resulting data set to a second AI layer that flags anomalies, escalates them to a human reviewer, and asks whether any action is required." The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets—artwork, design documents, level layouts—and converse with the AI in plain English (or any natural language).
By feeding the system both textual descriptions and visual references, the AI constructs a semantic model of the game’s mechanics, narrative flow, and expected behaviours. This model becomes the foundation for all subsequent testing activities. Once the AI has a solid understanding of the title, developers define the testing scope. They can select traditional test cases—such as collision detection, UI responsiveness, or load handling—or craft novel scenarios that play to the strengths of the AI, like exploratory testing of emergent gameplay loops.
Holmgard stresses that human expertise remains essential at this stage: "You need to know how to phrase prompts and guide the AI so it performs the right actions. The better the prompt, the more useful the output." During a test run, the system captures a wealth of data: textual logs, video clips that serve as visual evidence, and performance metrics. This package is then passed to a human QA specialist who reviews the findings, validates whether each flagged item is truly a defect, and decides on the appropriate remediation path. Holmgard notes that distinguishing a bug from an intentional feature can be especially tricky for an algorithm, and that ongoing feedback from human testers helps the AI fine‑tune its judgments over time.
Side sees this human‑in‑the‑loop model as an opportunity to upskill its workforce. Martin McBride, Side’s chief commercial officer, envisions QA staff evolving into "AI engineers" who design prompts, interpret AI‑generated insights, and oversee the integration of automated testing into broader production pipelines. "Instead of a career that ends at senior tester, our people can move into higher‑value roles, earn better salaries, and even bring those jobs back to regions where they were previously outsourced because low‑cost labor made manual testing unattractive," McBride says.
The AI also promises to make the day‑to‑day work of testers more rewarding. Repetitive smoke tests and regression checks, which consume a large portion of a tester’s schedule, can be offloaded to the machine.
This frees humans to focus on strategic analysis, creative test design, and deep gameplay evaluation—tasks that are more intellectually stimulating and career‑advancing. Benedikte Mikkelsen, Modl.ai’s chief product officer, adds, "You don’t need a person to repeat the same flow thousands of times. Let them concentrate on assessing the fun factor, exploring edge cases, and delivering insights that only a human can provide." Both companies are clear that AI will not replace QA professionals but will augment them, moving the workforce up the value chain.
McBride emphasizes that price pressures in certain low‑cost regions make it impossible to eliminate human testing entirely. By equipping testers with AI tools, Side can retain talent longer, offer clearer progression paths, and maintain a higher overall quality bar. Side’s chief operating officer Oliver Deslandes points out that the current iteration of the technology works best with games that have relatively simple, repeatable mechanics—puzzle titles, card games, and other low‑velocity experiences.
Fast‑paced 3D open‑world adventures or fighting games present challenges that the system will address in later phases. "We are targeting clients who are willing to join us on this journey," Deslandes says. "Some will demand immediate cost savings, others will be interested in the long‑term strategic advantage of AI‑enhanced QA." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles high‑volume, low‑complexity testing while seasoned human specialists interpret results, provide nuanced feedback, and continuously train the AI.
The collaboration reflects a broader industry trend: leveraging machine learning to reduce manual toil, improve defect detection, and ultimately deliver higher‑quality gaming experiences without discarding the indispensable insights of experienced QA professionals. The six‑month MOU will serve as a proving ground for this approach, with the hope that successful pilots will pave the way for broader adoption across the gaming sector.