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‑enhanced quality‑assurance (QA) platform that blends Side’s deep testing pedigree with Modl.ai’s cutting‑edge machine‑learning capabilities. Modl.ai, a relatively new entrant to the gaming industry, was founded in 2018 and secured $1.7 million in seed financing two years later. Its founders have spent the past few years refining AI tools that can automatically locate bugs, but early versions required developers to hand over entire game builds, source code, or SDK plugins.
That level of integration proved cumbersome and, as co‑founder Christoffer Holmgard admits, “became difficult over time.” The new solution eliminates the need for any internal code access. Instead, developers deliver the same package they would eventually give to a human tester or to a consumer.
The AI runs the game on a device that mirrors the target hardware, watches the screen, simulates player inputs, records every on‑screen event, and then passes the resulting data set to a second AI model that flags anomalies, classifies them as potential bugs, and escalates the most serious findings to a human reviewer. The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets—artwork, design documents, level layouts—and converse with the AI in plain English (or any natural language). By feeding images, text, and other media, the system constructs a semantic model of the game’s mechanics, narrative flow, and visual style. Holmgard explains that this conversational approach allows the AI to “understand” the game without ever seeing the underlying code.
Once the knowledge base is in place, developers specify the testing objectives. These can range from traditional test cases—such as verifying that a menu opens correctly—to more exploratory scenarios that are deliberately crafted to exploit the AI’s strengths. Holmgard notes that human expertise is still required at this stage to phrase prompts in a way the AI can interpret correctly, effectively translating QA intent into machine‑readable instructions. When a test run (or “session,” as Modl.ai calls it) is executed, the system captures a wealth of data: textual logs, video snippets that serve as visual evidence, and performance metrics.
This package is handed over to a human QA specialist who reviews the findings, decides which issues merit further investigation, and provides feedback. That feedback loop is crucial because it teaches the AI to differentiate between genuine bugs and intentional features. Over time, the model becomes attuned to the subtle design choices that only seasoned game designers would recognize. Side sees this collaborative human‑AI loop as a way to elevate its workforce.
Chief Commercial Officer Martin McBride envisions QA staff transitioning into “AI engineers,” a role that commands higher salaries and offers clearer career progression. By automating repetitive smoke tests and routine verification steps, the technology frees testers to focus on strategic analysis, gameplay assessment, and creative testing—tasks that add real value and are less likely to be outsourced to low‑cost regions. McBride adds that the shift could even reverse the historic offshoring of QA jobs.
When employees move up the value chain, they become indispensable, allowing Side to bring talent back to markets that previously lost testing positions due to price pressure. The company believes this will result in more fulfilling careers, as testers can engage in higher‑order problem solving rather than monotonous click‑throughs. Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes the sentiment, pointing out that many current tests are “very tedious” and could be performed more efficiently by an algorithm. She argues that the real benefit of AI lies in freeing humans to evaluate the gameplay experience itself, conduct exploratory testing, and apply creative thinking—areas where human intuition still outperforms machines.
Both executives stress that AI is not a replacement for QA professionals but a tool that “moves people up the value chain.” McBride notes that testing costs remain low in certain regions, making a full automation strategy economically unrealistic. Instead, the partnership aims to augment human testers with AI‑driven insights, extending their careers and enhancing overall product quality. Side’s Chief Operating Officer Oliver Deslandes is realistic about the current scope of the technology. At present, the AI works best with simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games.
He explains that the system needs predictable, slower‑moving environments to reliably capture and interpret screen data. Nonetheless, Deslandes believes the platform will evolve, and early adopters willing to pilot the solution will help shape its future capabilities. In summary, the Side‑Modl.ai collaboration aims to create a hybrid QA ecosystem where AI handles the grunt work of data collection and initial bug detection, while seasoned human testers provide contextual judgment and continuous training for the models. The partnership reflects a broader industry trend: leveraging machine learning to increase efficiency, reduce human error, and ultimately deliver higher‑quality games to players worldwide, all while preserving the essential human element that understands nuance, intent, and creative design.