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 binds the two companies to collaborate on an AI‑enhanced quality‑assurance (QA) platform that aims to blend the strengths of each partner into a single, innovative offering for the gaming industry.
Modl.ai, a relatively new player in the games market, was founded in 2018 and secured $1.7 million in seed capital two years later. While Side brings decades of experience in delivering testing and support services, Modl.ai contributes cutting‑edge machine‑learning expertise. "The MOU effectively brings the best of what Side has and the best of what Modl.ai has together in a unique solution," explains Martin McBride, Side’s chief commercial officer.
He adds that creating an AI‑augmented QA workflow has long been described as a "holy grail" for game developers, and the partnership believes it may finally have cracked the code. Historically, AI‑driven QA tools required developers to ship entire builds—or even source code—to the testing vendor. Modl.ai’s earlier approach relied on an integration plug‑in and SDK, forcing studios to expose internal assets and creating considerable friction. "It became difficult over time," recalls Modl.ai founder Christoffer Holmgard.
The new system, however, eliminates the need for any deep integration. Instead, the product is handed to the AI exactly as a human tester would receive it: installed on a device that mirrors the end‑user’s hardware, then run while the AI watches the screen, simulates inputs, and records every observable event.
The workflow begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, and other relevant material, then converse with the AI in natural language to teach it the fundamentals of their title. "All of this is in natural language," Holmgard notes. "We just chat with the system about our game, but we're also adding in images, or we might upload our game design documents and so on." Once the AI has a baseline understanding, developers specify the testing objectives.
These can range from classic test cases—such as verifying menu navigation or level loading—to novel scenarios that are deliberately crafted to exploit the AI’s pattern‑recognition capabilities. Holmgard stresses that human expertise is still essential at this stage: "You need to adapt the prompting and the language to the AI agent to make it do the right things." During a test run, the AI plays the game, captures video snippets, logs textual output, and assembles a comprehensive data set.
After the session ends, a second AI layer analyses the collected information, flags potential defects, and escalates them to a human QA specialist for validation. "We get all the texts, and we get some video snippets that we call evidence along with all the log data and everything," Holmgard explains. The specialist reviews each report, decides whether the issue is a genuine bug, a feature, or a false positive, and then feeds that feedback back into the system. Over time, this iterative loop refines the AI’s ability to differentiate between intentional design choices and unintended errors.
One of the persistent challenges in game QA is distinguishing a bug from a feature—a nuance that often depends on design intent and player experience. The partnership plans to leverage continuous feedback from human testers to teach the AI these subtleties. "Some of it comes from the prompting. Some of it comes from that game knowledge that you've built up around the game ahead of time," Holmgard says.
"And then a good part of it also comes from feedback over time from the human testers." Beyond the technical advantages, Side envisions a broader impact on its workforce. By equipping testers with AI tools, the company hopes to elevate them from routine, repetitive tasks to roles that require higher‑order thinking, such as AI‑engineer or data‑analysis specialist.
"Instead of just doing testing and having a career at Side be a tester and moving up to senior tester, they can become an AI engineer," McBride asserts. This upskilling not only promises higher salaries but also opens pathways to bring jobs back to regions where low‑cost testing had previously been outsourced. The AI system also aims to make QA careers more fulfilling.
Repetitive smoke tests and verification steps, which can dominate a tester’s day, are automated, freeing staff to focus on strategic analysis, gameplay evaluation, and creative testing approaches. "By knocking these out and automating them, they get to have a more fulfilling career here with Side because they get to think more strategically," McBride explains.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "There are some very tedious tests that testers are performing right now. You don't really need a human doing the same flow over and over again. That person could be doing something much more valuable, like assessing the gameplay itself or doing more creative kinds of testing." Despite the optimism, both parties acknowledge that AI will not replace human QA workers.
The price point for manual testing in certain low‑cost regions remains low, making a complete automation shift impractical for now. Instead, AI serves as a force multiplier, allowing existing staff to stay relevant longer and transition into higher‑value roles. "There's always going to be an opportunity for people to do a lot of the testing work because the price point of testing right now is so low in certain countries that it's very hard to augment it completely away," McBride says.
"But by giving our people tool sets like this, I'm allowing them to further their career." Side’s philosophy toward AI can be summed up as a "sliding scale"—technology that can be dialed up or down depending on the task, but never fully replacing the human element. "There will always be a human in the loop; that's our ethos," McBride affirms. He believes that removing human error from repetitive tasks raises overall quality and efficiency, creating a virtuous cycle of improvement.
When asked about the types of games the solution currently supports, COO Oliver Deslandes admits 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 adventures or fighting games. "We have to focus on titles that are more appropriate. What we're aiming for at this stage is really clients that are willing to come on the journey with us," he says, noting that early adopters may have higher expectations for cost savings and rapid results. Deslandes emphasizes that the partnership is a long‑term experiment: "The reality is that it's a journey, but if you are at the table now with us and maybe doing some pilots, then you will see where this tech goes." In summary, the Side‑Modl.ai collaboration seeks to blend AI’s speed and consistency with human expertise, creating a hybrid QA model that reduces tedious work, improves defect detection, and opens new career pathways for testers.
By keeping a human in the loop and treating AI as a scalable aid rather than a replacement, the partnership aims to set a new standard for quality assurance in the gaming industry.