Side, a seasoned 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 paves the way for a joint effort to build an AI‑powered quality‑assurance (QA) platform that blends Side’s extensive testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities. Modl.ai, a relative newcomer to the gaming sector, was founded in 2018 and secured $1.7 million in seed financing two years later. While the firm has already experimented with AI‑enabled QA tools, earlier versions required developers to hand over entire game builds or even source code via plugins and SDKs.
This “integration‑heavy” approach created friction, as it forced studios to expose internal assets and made the process cumbersome over time, according to founder Christoffer Holmgard. The new solution eliminates that barrier. Holmgard explains that the system treats the game exactly as a human tester would receive it: a compiled build placed on a device that mirrors the end‑user environment.
The AI then runs the title, watches the screen, simulates key actions, and records a comprehensive data set of every event. A second AI layer analyses this data, flags potential defects, and escalates them to human reviewers for final validation. The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets—artwork, design documents, level files—and converse with the AI in plain language. This dialogue, enriched with images and textual specifications, helps the system construct an internal model of the game’s mechanics and objectives.
Once this knowledge base is established, QA teams define the test scope, ranging from conventional test cases to novel scenarios that play to the strengths of the AI engine. Human expertise remains essential at this juncture. Holmgard notes that crafting effective prompts and guiding the AI’s language model requires a nuanced understanding of both the game and the technology. After the AI executes the defined sessions—referred to as "runs"—it produces bug reports accompanied by video snippets, log files, and other evidence.
These artifacts are handed to seasoned QA specialists who assess the findings, decide on remediation priorities, and feed their decisions back into the system. One of the persistent challenges in automated testing is distinguishing a genuine bug from an intentional feature. Holmgard stresses that continuous feedback from human testers sharpens the AI’s judgment. Over time, as the system receives confirmations such as "this is a bug" or "this is an intended behavior," its predictive accuracy improves, making it increasingly attuned to the subtleties of game design.
Side envisions this collaboration reshaping the career trajectory of its QA workforce. Chief Commercial Officer Martin McBride describes a future where testers evolve into AI engineers, moving up the value chain and gaining higher‑paid, more strategic roles. By offloading repetitive, low‑value tasks to the AI—such as smoke tests and routine verification—human testers can focus on deeper analysis, creative exploration, and gameplay assessment.
This shift not only enhances job satisfaction but also opens the possibility of repatriating QA positions to regions where they have previously disappeared due to cost pressures. McBride adds that the AI tool reduces human error and fatigue. "People are prone to making mistakes when they have to repeat the same steps over and over," he says.
"By automating those monotone processes, we raise the overall quality of the output and free our staff to think strategically." Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes this sentiment, pointing out that many current tests are mind‑numbing and could be performed more efficiently by machines. She argues that reallocating human effort toward evaluating gameplay, designing novel test scenarios, and providing creative insights delivers a clear value proposition for studios. Both leaders are careful to stress that AI will not replace QA professionals.
Instead, it acts as an augmentative tool that elevates the role of humans within the testing pipeline. McBride notes that testing rates in certain low‑cost regions are so low that a complete AI takeover would be economically unfeasible. Providing testers with advanced AI‑assisted tools, however, extends their career longevity and allows Side to retain talent longer.
The partnership also frames AI adoption as a "sliding scale"—a spectrum where the degree of automation can be adjusted based on project needs, while a human remains in the loop at every stage. This philosophy underpins Side’s approach: leverage AI to eliminate tedious work and reduce mistakes, but never eliminate the critical human judgment that defines quality in interactive entertainment. When asked about the types of games the system can currently handle, Chief Operating Officer Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, graphically intensive 3D open‑world or fighting games remain a longer‑term goal.
Deslandes emphasizes that early adopters will likely be studios willing to pilot the technology and tolerate a learning curve while the solution matures. In summary, the Side‑Modl.ai MOU represents a strategic blend of seasoned QA know‑how and emerging AI capabilities.
By creating a workflow that respects the need for human insight while automating the most repetitive aspects of testing, the partnership aims to deliver higher‑quality game releases, more rewarding careers for QA staff, and a scalable model that can gradually expand to more complex game genres as the technology evolves.