Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Danish 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 deep industry experience with Modl.ai’s cutting‑edge machine‑learning capabilities.
Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed financing two years later. Its chief commercial officer, Martin McBride, explains that the partnership “effectively merges the strongest assets of Side and Modl.ai into a distinctive solution.” He adds that the gaming industry has long searched for a "holy‑grail" AI approach that can truly augment QA services, and that the new collaboration aims to fill that gap. Earlier AI‑based QA tools—including Modl.ai’s own prior offering—required developers to hand over full builds or even source code to a testing vendor. Those integrations, typically delivered via plugins and SDKs, introduced considerable friction and, as founder Christoffer Holmgard notes, “became difficult over time.” The revamped system eliminates the need for any internal code access.
Instead, developers deliver the game exactly as they would to a human tester or, ultimately, to the consumer. The AI then runs the title on a comparable device, watches the screen, simulates key inputs, records every observable event, and passes the resulting data set to a second AI layer that flags potential bugs.
That layer escalates findings to a human reviewer for final validation. The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, or any other relevant material and converse with the AI in plain language.
Holmgard describes the process: “We just chat with the system about our game, but we also feed it images and design docs, so it builds a contextual understanding.” Once the AI has a solid grasp of the title, developers specify the testing scope—ranging from conventional test cases to novel scenarios that are especially suited to AI‑driven exploration. Human expertise remains essential at this stage. "You need a skilled tester to shape the prompts and guide the AI so it performs the right actions," Holmgard explains. After the AI executes a session—referred to as a “run”—it compiles bug reports that include textual descriptions, short video clips as evidence, and comprehensive log data.
These artifacts are handed to a human QA specialist who reviews them, decides on their relevance, and determines the next steps in the production pipeline. One of the most nuanced challenges for an automated system is distinguishing a genuine defect from an intentional feature.
Holmgard points out that feedback loops from human testers are crucial: “Testers label each report as a bug, not a bug, or a feature, and over time the AI learns these subtleties.” This continuous learning process refines the AI’s judgment and reduces false positives. Side envisions a broader transformation of the QA workforce.
By equipping testers with AI tools, the company hopes to elevate them from routine test execution to roles akin to AI engineers. McBride says this shift “moves our people up the value chain,” enabling higher salaries, new career trajectories, and the possibility of bringing jobs back to regions where low‑cost testing had previously migrated away.
The automation of repetitive smoke tests and other monotonous checks frees QA professionals to focus on strategic analysis, creative testing, and deeper gameplay evaluation. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “Tedious, repetitive tests don’t need a human to repeat them endlessly. Instead, a tester can spend time assessing gameplay feel or designing innovative test scenarios, which is where real value lies.” Both executives stress that AI is not a replacement for human QA staff but a catalyst for professional growth. McBride notes that the current low price points for manual testing in certain countries make a full AI takeover impractical.
However, by providing advanced toolsets, Side can retain talent longer, upskill employees, and maintain a higher standard of quality. The partnership’s philosophy is summed up by McBride’s description of AI as a “sliding scale”: there will always be a human in the loop. "That’s our ethos," he says.
"The tool lets us leverage AI’s strengths—speed, consistency, error reduction—while keeping human judgment at the core of decision‑making." When asked about the types of games the solution can currently handle, COO Oliver Deslandes is candid. The technology is best suited for 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 team is focusing on projects where the AI can demonstrate clear value while partners are willing to embark on a pilot journey. "It’s a gradual process, but those who join us early will see how the technology evolves," Deslandes adds.
In summary, the Side‑Modl.ai MOU aims to create a hybrid QA ecosystem where AI handles the heavy lifting of data collection and initial bug detection, and seasoned human testers apply their expertise to validate, prioritize, and refine those findings. The collaboration aspires not only to improve efficiency and accuracy but also to reshape career pathways within the QA industry, offering a more rewarding and technically sophisticated future for testers worldwide.