Side, a long‑standing 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‑driven quality‑assurance (QA) platform that blends the strengths of both organisations.
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. Side, by contrast, brings decades of experience in delivering services across multiple industries.
"The MOU essentially fuses Side’s deep operational expertise with Modl.ai’s cutting‑edge AI technology into a single, differentiated solution," explains Martin McBride, Side’s chief commercial officer. "For the games industry, the holy grail has been a way to augment QA with AI in a reliable, scalable manner. We believe we’ve finally cracked that code." Earlier AI‑enabled QA tools, including Modl.ai’s own first‑generation offering, required developers to hand over full builds or even source code to the testing firm. Those integrations relied on plugins, SDKs, and extensive code access, creating friction and slowing adoption.
"It became cumbersome and eventually untenable," recalls Modl.ai founder Christoffer Holmgard. The new approach eliminates the need for any internal code exposure. Instead, developers supply the product exactly as they would to a human tester—installed on a device that mirrors the end‑user environment.
The AI then runs the game, watches the screen, simulates inputs, records a comprehensive data set, and passes that information to a second AI layer that flags potential defects. Human reviewers then assess the AI‑generated reports to decide on further action. The process begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, and other relevant materials, then converse with the AI in plain English (or any natural language) to teach it the game's mechanics, narrative, and intended player experience. "We simply chat with the system, feeding it images and documentation, and it constructs an internal model of the game," Holmgard explains.
Once the AI has a solid understanding, developers define test objectives—ranging from classic test cases to novel scenarios that leverage the AI’s strengths. This step still demands human insight to craft effective prompts and guide the AI toward meaningful actions.
"Human expertise is crucial for shaping the language and intent that the AI interprets," Holmgard notes. During a test run, the system captures textual logs, video snippets (referred to as "evidence"), and a full suite of telemetry data. These artifacts are compiled into a bug report that is handed off to a seasoned QA specialist.
The specialist reviews the findings, determines whether each issue is a genuine defect, a feature, or an acceptable variance, and provides feedback that the AI ingests to refine its future judgments. Over time, this feedback loop tunes the model to better distinguish between intentional design choices and unintended bugs. One of the persistent challenges in automated testing is discerning a bug from a feature, especially in games where emergent behavior is common. Holmgard stresses that continuous input from human testers is essential for teaching the AI the subtle nuances of game design.
"Prompt engineering, pre‑built game knowledge, and ongoing tester feedback all combine to make the system smarter," he says. Side envisions this technology reshaping the career trajectory of QA professionals.
Rather than remaining confined to repetitive test execution, staff can evolve into AI engineers who design prompts, interpret AI outputs, and oversee the feedback loop. "Our people can climb the value chain, earn higher salaries, and even bring jobs back to regions where low‑cost testing has migrated away," McBride adds. By automating monotonous smoke tests and routine verifications, the AI frees testers to focus on strategic analysis, creative exploration, and higher‑order problem solving.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are mind‑numbingly repetitive. Automating those frees humans to evaluate gameplay, devise inventive test scenarios, and add real value. That’s the core proposition of AI in QA." Both executives are clear that AI will not replace QA staff but will augment them. The price of manual testing in certain low‑cost regions remains so low that a complete AI takeover is economically impractical.
Instead, AI serves as a force multiplier, allowing seasoned testers to extend their careers and contribute more strategically. "There will always be a human in the loop—that’s our ethos," McBride affirms. "AI reduces tedious work and human error, raising the overall quality of the process." When asked about the types of games the solution currently supports, COO Oliver Deslandes admits the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable, slower mechanics. Fast‑paced 3D open‑world or fighting games remain a future target.
"We’re focusing on projects where the AI can demonstrate clear value now, while we continue to iterate and expand its capabilities," he says. Clients willing to pilot the system will help shape its evolution and determine how quickly it can tackle more complex genres. In summary, the Side‑Modl.ai partnership aims to create a hybrid QA model where AI handles bulk, repetitive testing and humans provide the critical judgment and creativity that machines lack. By combining natural‑language interaction, automated gameplay observation, and a robust feedback mechanism, the collaboration seeks to deliver faster, more accurate bug detection while elevating the role of QA professionals within the industry.