Side, a veteran 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‑enhanced quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities. Modl.ai, a relatively new entrant to the gaming sector, was founded in 2018 and secured $1.7 million in seed funding two years later.
While Side has been delivering testing services for many years, Modl.ai brings a fresh perspective on how AI can be used to augment, rather than replace, human QA work. “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. “One of the crown‑jewel, holy‑grail experiences for the games industry right now has been using AI technology to better augment QA services.
No one’s really found a great solution for that. We think we have.” Earlier AI‑driven QA tools—including earlier versions of Modl.ai’s own platform—required developers to ship entire builds or even provide source code to the testing vendor.
Those integrations relied on plugins, SDKs, and deep access to a game’s internals, creating friction and limiting scalability. According to Modl.ai founder Christoffer Holmgard, that approach “became difficult over time.” The new system eliminates the need for any internal access. “We take the product as you would give it to a QA tester, as you would eventually give it to the consumer,” Holmgard says. “You put it on a very similar or even the same device.
Then our solution plays the game by looking at the screen, simulating key processes, generating a data set of everything that happens, and then hands it over to a second AI system that has a look at it and identifies bugs, raises it up to the human level and asks whether this is something we should act on.” The first phase of the workflow is what Modl.ai calls building "game knowledge." Developers upload assets, design documents, screenshots, or even converse with the AI in natural language to teach the system about the game’s mechanics, narrative, and visual style. "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 UI elements or checking level loading times—to more exploratory scenarios that are specifically crafted to leverage the AI’s pattern‑recognition strengths. Holmgard stresses that human insight remains crucial at this stage: "This is where you need some human expertise to adapt the prompting and the language to the AI agent to make it do the right things." During a testing session, which Modl.ai refers to as a "run," the system records every interaction, captures video snippets as evidence, and logs detailed telemetry. The output includes textual bug descriptions, visual proof, and full log files, all of which are forwarded to a human QA specialist for review.
"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. "This goes to a human QA specialist, an expert. They review it, and then they decide what happens with it for the rest of our production." One of the persistent challenges in automated testing is distinguishing a genuine bug from an intended feature.
Holmgard says that continuous feedback from human testers helps the AI refine its judgment. "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," he says. "And then a good part of it also comes from feedback over time from the human testers. They will provide feedback to the bug reports and say: ‘This was a bug; this was not a bug; this was not a bug for this reason,’ and so, over time, the system gets attuned." Side sees the partnership as a pathway for its QA workforce to evolve into AI engineers. By moving staff up the value chain, the company hopes to create higher‑paying, more strategic roles that can be located in regions where testing jobs have traditionally migrated away due to cost pressures.
"One of the cool parts about programs like this is that we get to move our people up the value chain," McBride says. "Instead of just doing testing and having a career at Side being a tester and moving up to senior tester, they can become an AI engineer.
It allows our people to get paid more, opens new career paths, and potentially brings jobs back to countries where they have historically disappeared." McBride also points out that AI can make QA careers more fulfilling by automating repetitive, low‑value tasks. "For every new build you have to do smoke tests, many verifications, and so on. By knocking these out and automating them, testers can think more strategically, focus on analysis, and grow their careers," he 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. That, I think, is the value proposition of using AI." Both leaders stress that AI will not replace QA professionals but will augment them.
McBride notes that testing price points remain low in certain regions, making a complete AI takeover impractical. Instead, the technology serves as a toolset that extends careers and improves quality. "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," he says. "But by giving our people tool sets like this, I’m allowing them to further their career, have a longer career, and keep them for longer." Side’s philosophy is that AI operates on a "sliding scale" with a constant human in the loop—an ethos that underpins the partnership.
"That’s sort of our ethos," McBride affirms. "This tool really does allow our people to take the best of what AI can offer, reduce the burden, and also reduce mistakes. A lot of this work is tedious and prone to human error. You take that out of the equation, and everything just ratchets up a notch and becomes better." When asked about the types of games the solution currently supports, COO Oliver Deslandes admits that the technology 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 at this stage," he says. "The reality is that it’s a journey, but if you are at the table now with us and maybe doing some pilots, you will see where this tech goes."