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 partnership is designed to develop a joint AI‑driven quality‑assurance (QA) platform that blends Side’s deep testing pedigree with Modl.ai’s cutting‑edge machine‑learning capabilities.
Modl.ai, although a relatively new name in the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later. The company’s ambition is to reshape how game developers locate and fix bugs, moving away from cumbersome, code‑centric approaches toward a more streamlined, user‑focused workflow. "The MOU essentially merges the strongest assets of Side with the most advanced AI tools from Modl.ai into a single, differentiated solution," explains Martin McBride, Side’s chief commercial officer.
"For the games industry, one of the holy‑grail challenges has been leveraging AI to truly augment QA services. No one has cracked that code yet, and we believe we are on the brink of doing so." Earlier AI‑enabled QA solutions—some of which were built by Modl.ai itself—required developers to ship complete builds or even share source code with the testing vendor. Those integrations relied on plugins and SDKs that created significant friction and, over time, proved difficult to maintain, according to Modl.ai founder Christoffer Holmgard. Holmgard emphasizes that the new system eliminates the need for any internal access.
"We treat the product exactly as you would hand it to a QA tester—or eventually to a consumer. You load it onto a comparable device, and our AI watches the screen, simulates key actions, records every event, and then hands the data to a second AI layer that flags potential bugs. That layer then escalates the findings to a human reviewer for validation." The first phase, termed "building game knowledge," involves developers uploading assets, design documents, screenshots, and other relevant materials.
They then converse with the AI in plain natural language, allowing the system to construct a contextual model of the game’s mechanics, narrative, and visual style. "All communication is in natural language," Holmgard notes. "We chat with the system about the game, feed it images, design docs, and so on.
This creates a shared understanding that the AI can draw upon when testing." Once the AI has a solid grasp of the title, developers specify the test objectives. These can range from traditional test cases—such as checking menu navigation or collision detection—to more exploratory scenarios that are uniquely suited to AI‑driven exploration. "Human expertise is still crucial at this stage to craft the right prompts and guide the AI toward the desired outcomes," Holmgard adds.
"The quality of the AI’s work depends heavily on how well we phrase the instructions." During a testing session—referred to by Modl.ai as a "run"—the system captures textual logs, short video clips (the so‑called "evidence"), and comprehensive telemetry. This bundle is then passed to a seasoned QA specialist who reviews the findings, decides which issues merit further action, and integrates the results into the development pipeline.
One of the perennial challenges in game QA is distinguishing a genuine bug from an intentional feature. Holmgard explains that continuous feedback from human testers helps the AI refine its judgment. "We provide the AI with labels—'this is a bug', 'this is not a bug', and the rationale behind each decision.
Over time, the model learns these nuances and becomes more accurate." Side envisions a future where QA professionals transition into AI engineers, thereby elevating the skill set and value of their workforce. McBride argues that this shift can reverse the off‑shoring trend that has plagued low‑cost testing hubs for years. "By moving our people up the value chain, we can bring higher‑paid, more strategic roles back to regions that previously lost jobs due to price pressures," he says. "Our staff can evolve from performing repetitive checks to designing AI prompts, interpreting complex data, and shaping overall test strategies." McBride also stresses that AI is intended to make QA careers more rewarding, not to replace humans.
"The tool automates the monotonous smoke tests and regression checks that bog down testers," he explains. "When those tasks are handled by AI, our people can focus on strategic analysis, gameplay assessment, and creative testing—activities that foster growth and job satisfaction." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are mind‑numbing repetitions. You don’t need a human to click the same sequence a hundred times.
Freeing testers from that drudgery lets them concentrate on higher‑order evaluation, such as assessing game feel or exploring edge‑case scenarios. That’s the core value proposition of AI in QA." Nevertheless, McBride is clear that AI will never fully supplant human testers. The price point for low‑cost testing in certain regions remains so low that a complete AI takeover is economically impractical.
Instead, the technology serves as an enhancer, allowing testers to extend their careers and take on more sophisticated responsibilities. "Our philosophy is a sliding scale," McBride says. "There will always be a human in the loop. That’s our ethos.
AI reduces the repetitive load and minimizes human error, raising the overall quality of the testing process." When asked about the types of games the solution currently supports, COO Oliver Deslandes admits that the platform is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced 3D open‑world or fighting games remain a longer‑term goal.
"We’re focusing on games that aren’t too rapid or complex right now," Deslandes explains. "Clients willing to pilot this technology with us will help shape its evolution.
Some will demand immediate cost savings, while others will be more interested in long‑term innovation. It’s a journey, and early adopters will see where the tech can go." In summary, the Side‑Modl.ai collaboration aims to create a hybrid QA ecosystem where AI handles the bulk of repetitive testing, while seasoned human experts provide oversight, strategic insight, and continuous feedback. By doing so, both companies hope to deliver faster, more accurate bug detection, elevate the skill set of QA personnel, and ultimately bring greater value to game developers worldwide.