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‑driven quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities.
Modl.ai, a relative newcomer to the gaming industry, was founded in 2018 and secured $1.7 million in seed capital two years later. While the firm has already experimented with AI‑enabled QA tools, those early versions required developers to hand over entire game builds or even source code through a plugin‑and‑SDK integration.
This approach proved cumbersome, creating friction for development teams and limiting scalability, according to founder Christoffer Holmgard. The new solution, Holmgard explains, eliminates the need for any internal access to the game’s code. "We treat the product exactly as you would hand it to a QA tester—or eventually to the consumer," he says.
"The game runs on a device that mirrors the target platform, our system watches the screen, simulates key inputs, records every event, and then passes the resulting data set to a second AI layer that flags potential bugs. That layer escalates the findings to a human reviewer who decides whether action is required." The first phase of the workflow is what Modl.ai calls building "game knowledge." Developers upload assets, design documents, screenshots, and other relevant materials, then converse with the AI in natural language to teach it the fundamentals of their title.
"All interactions are plain English," Holmgard notes. "You can chat about mechanics, show images, or feed in design docs, and the model gradually constructs an internal representation of the game." Once the AI has a basic understanding, the development team defines the testing scope. This can range from traditional test cases—such as verifying UI elements or level loading—to novel scenarios that play to the strengths of the AI system.
Holmgard stresses that human expertise is still essential at this stage: "You need to craft prompts and guide the AI so it performs the right actions. It’s a collaborative choreography between people and machine." During a test run, the system captures text logs, video snippets (referred to as "evidence"), and comprehensive telemetry. These artifacts are bundled into a bug report and handed to a seasoned QA specialist.
The specialist reviews the findings, determines their validity, and decides the next steps in the production pipeline. One of the persistent challenges in automated testing is distinguishing a genuine defect from an intentional feature. Holmgard says that continual feedback from human testers helps the AI refine its judgment over time. "Prompt engineering, pre‑built game knowledge, and iterative human feedback all teach the model what constitutes a bug versus a design choice." Side envisions a future where QA personnel evolve into AI engineers, thereby reshaping the global distribution of testing jobs.
Chief Commercial Officer Martin McBride explains, "Instead of a linear career that ends at senior tester, our people can transition into AI‑engineering roles, earn higher salaries, and open new career pathways. This also lets us bring jobs back to regions where they have disappeared due to low‑cost labour pressures, because the value‑added work moves up the chain." McBride adds that AI will make the day‑to‑day work of testers more rewarding.
Repetitive smoke tests and routine verifications currently consume a large portion of a tester’s time, limiting strategic thinking. By automating these monotonous tasks, AI frees testers to focus on analysis, creative testing, and higher‑order problem solving.
"When the tool handles the boring bits, our staff can think strategically and grow professionally," he says. Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes this sentiment: "Many current tests are mind‑numbing loops that a human doesn’t need to repeat. By offloading them to AI, testers can concentrate on gameplay assessment and inventive testing approaches, which is the real value proposition of AI in QA." Both leaders are quick to stress that AI is not a replacement for human QA talent.
McBride notes that testing costs remain low in certain regions, making a full AI takeover impractical. Instead, the technology serves as a toolset that elevates workers, extending their careers and keeping them engaged. "There will always be a place for people in the loop," he affirms, describing AI as a "sliding scale" where the proportion of automation can be adjusted but never fully eliminates human oversight. When asked about the types of games the system currently supports, COO Oliver Deslandes admits the focus is on simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games.
"We’re targeting projects where the gameplay loop is steady enough for the AI to learn and act reliably," he says. "Clients willing to pilot this technology with us will help shape its evolution, and as the model matures we’ll expand into more complex genres." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles high‑volume, low‑complexity testing while seasoned humans provide the critical judgment and creative insight that only experience can deliver. The six‑month MOU will serve as a proving ground for this approach, potentially redefining how quality assurance is performed across the gaming industry and offering a blueprint for other sectors to follow.