Side, a veteran services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Danish 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 expertise 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 funding two years later. While Side has long been embedded in the services arena, the partnership aims to combine the strengths of both companies into a single, differentiated offering.
“The MOU essentially merges the best of Side’s testing pedigree with the most advanced AI tools Modl.ai has developed,” explains Martin McBride, Side’s chief commercial officer. “The industry has been searching for a ‘holy‑grail’ AI‑driven QA solution for years.
We believe we’re on the cusp of delivering it.” Historically, AI‑based QA tools required developers to hand over full game builds—or even source code—to the testing provider. Modl.ai’s earlier approach relied on an integration plug‑in and SDK, demanding developers expose internal assets, which proved cumbersome and created friction over time, according to founder Christoffer Holmgard. The new system abandons that model entirely.
Instead, it treats the game exactly as a human tester would receive it: a compiled build ready to run on a target device. The AI watches the screen, mimics player inputs, logs every interaction, and then passes the resulting data to a second AI layer that flags potential defects.
Human reviewers then assess those flags to decide whether any action is required. 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 English.
This natural‑language interaction helps the system construct a contextual model of the game’s mechanics, narrative, and visual style. Once that foundation is in place, developers specify the testing objectives—ranging from conventional test cases to novel scenarios that play to the AI’s strengths. Holmgard stresses that crafting effective prompts still needs human insight.
“You have to translate your testing goals into language the AI understands,” he notes. “That translation step is where seasoned QA professionals add value.” When a test run—referred to as a “session” by Modl.ai—concludes, the platform generates a comprehensive bug report. The report includes textual descriptions, short video clips as evidence, and full log files.
These artifacts are handed to a human QA specialist, who reviews them, validates the findings, and determines the next steps in the production pipeline. One of the trickier challenges for any automated system is distinguishing a genuine bug from an intentional game feature. Holmgard explains that the AI learns from a combination of initial prompting, the pre‑built game knowledge, and ongoing feedback from human testers. Over time, as testers label issues as "bugs" or "not bugs" and provide reasons, the AI’s internal model becomes more attuned to the nuances of game design.
Side envisions this collaboration reshaping the career trajectory of its QA staff. Rather than remaining in repetitive testing roles, employees could evolve into AI engineers, overseeing prompt engineering, model training, and system integration.
"When we lift our people up the value chain, we not only increase their earning potential, but we also create pathways to bring jobs back to regions that have lost testing work due to cost pressures," McBride says. He adds that automating monotonous test cases frees testers to focus on strategic analysis, creative exploration, and higher‑order problem solving, making their work more rewarding.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are mind‑numbingly repetitive. An AI can handle those loops, freeing humans to evaluate gameplay feel, balance, and emergent behaviors—tasks that truly require human judgment." Both leaders are clear that AI will not replace QA professionals but will augment them.
McBride points out that testing costs remain low in certain markets, making a wholesale AI takeover impractical. Instead, the technology serves as a force multiplier, allowing testers to stay relevant longer and to command higher salaries.
Side’s philosophy treats AI as a “sliding scale” where human involvement is always present. "Our ethos is that there will always be a human in the loop," McBride asserts.
"The AI reduces tedious work and human error, raising overall quality while preserving the essential expertise of our people." When asked about the types of games the solution currently supports, COO Oliver Deslandes admits the focus is on simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, graphically intensive 3D open‑world or fighting games remain a future target. "We’re looking for partners willing to pilot this technology and help us refine it for more complex genres," Deslandes says. He concludes that the journey is just beginning, and early adopters will witness the evolution of AI‑driven QA firsthand.
In summary, the Side‑Modl.ai partnership aims to deliver a hybrid QA model that leverages AI for efficiency while retaining human expertise for nuance and judgment. By re‑skilling testers as AI engineers and focusing on manageable game genres first, the collaboration hopes to set a new standard for quality assurance in the gaming industry.