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 build an AI‑powered quality‑assurance (QA) platform that blends Side’s extensive testing expertise with Modl.ai’s cutting‑edge machine‑learning technology.
Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later. Its founders have long pursued the elusive goal of using AI to augment, rather than replace, traditional QA services—a challenge that many in the industry still consider unsolved. “The MOU effectively brings together the best of Side and the best of Modl.ai into 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 is using AI to enhance QA.
No one has nailed a great solution yet. We think we have.” Earlier AI‑driven QA tools, including Modl.ai’s own previous offering, required developers to hand over entire game builds or even source code so that the system could run its tests. That approach proved cumbersome, creating friction for studios and slowing down iteration cycles. According to Modl.ai founder Christoffer Holmgard, the new architecture eliminates the need for deep integration.
“We treat the product exactly as you would hand it to a QA tester or eventually to a consumer,” Holmgard says. “You load it onto a comparable device, and our solution watches the screen, simulates key inputs, records everything that happens, and then passes that data to a second AI layer that flags bugs, escalates them to a human reviewer, and asks whether action is required.” The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets—artwork, design documents, even video clips—and converse with the AI in plain language.
“All of this is natural language,” Holmgard notes. “We chat with the system about our game, we feed it images, design specs, and other materials, allowing it to develop an internal model of the title.” Once the AI has a basic understanding, developers specify the testing objectives. These can range from classic test cases to novel scenarios that are particularly suited to AI‑driven exploration.
“Human expertise is still needed to craft the right prompts and guide the AI agent to do the right things,” Holmgard adds, highlighting the collaborative nature of the process. During a test run—referred to by Modl.ai as a “session”—the system generates bug reports, complete with textual descriptions, short video snippets (the so‑called “evidence”), and comprehensive log data. These artifacts are handed off to a human QA specialist for review.
“The specialist decides what to do with each report in the broader production pipeline,” Holmgard explains. One of the trickier aspects for an autonomous system is distinguishing a genuine bug from an intended feature.
Holmgard says that ongoing feedback from human testers helps the AI refine its judgment. “Some learning comes from the prompting, some from the pre‑built game knowledge, and a significant portion comes from human feedback on each bug report—‘this is a bug, this is not, here’s why.’ Over time the model becomes attuned to those nuances.” Side envisions a future where QA professionals evolve into AI engineers, shifting the value chain upward. McBride argues that this transition can revitalize jobs that have migrated away from lower‑cost regions due to price pressures.
“Instead of a linear career path from tester to senior tester, our people can become AI engineers, earn higher salaries, and potentially bring jobs back to countries where they have historically disappeared.” He also stresses that AI is meant to free testers from repetitive, low‑value tasks, allowing them to focus on strategic analysis and creative problem‑solving. “Every new build requires smoke tests and countless verifications that bog down workflow,” McBride says.
“By automating those routine checks, testers can engage in more fulfilling work, think strategically, and grow their careers.” Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “There are very tedious tests that humans repeat over and over. An AI can handle those flows, freeing the person to assess gameplay, explore creative testing angles, and add real value.” Both executives agree that AI will never fully replace QA staff; rather, it will elevate them. “The price point for testing in some regions is so low that you can’t eliminate human involvement entirely,” McBride notes.
“Providing our people with tools like this extends their careers, moves them up the value chain, and keeps them engaged longer.” Side’s philosophy frames AI as a “sliding scale” where a human is always present in the loop. “That’s our ethos,” McBride affirms. “The tool lets us harness AI’s strengths—speed, consistency, error reduction—while still relying on human judgment for the nuanced decisions that matter.” When asked about the types of games the solution currently supports, Side’s chief operating officer Oliver Deslandes is candid: the technology is best suited for simpler titles—puzzles, card games, or other experiences with repeatable mechanics—not fast‑paced 3D open‑world or fighting games. “We’re focusing on projects where the mechanics are predictable enough for the AI to learn effectively,” he says.
“Clients who are willing to pilot this journey with us will help shape the next generation of AI‑enhanced QA.” In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles the heavy lifting of data collection and initial bug detection, while seasoned human testers provide the critical contextual insight needed to turn those findings into actionable improvements. The collaboration promises to raise the skill ceiling for QA professionals, reduce repetitive workload, and ultimately deliver higher‑quality gaming experiences.