Side, a veteran services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Modl.ai, a Danish artificial‑intelligence start‑up that has been making inroads into the gaming sector since its 2018 launch and a $1.7 million seed round in 2020. The agreement commits the two companies to co‑develop an AI‑enhanced quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities.
Martin McBride, Side’s chief commercial officer, describes the partnership as a marriage of the best assets each party brings to the table. “One of the holy‑grail experiences the games industry is chasing right now is a way to use AI to augment QA services without sacrificing accuracy,” he explains. “Nobody has cracked that problem yet, but we believe this collaboration does.” Historically, AI‑driven QA tools—including earlier versions from Modl.ai—required developers to hand over full game builds or even source code so the system could run internal analyses.
That approach proved cumbersome; developers had to integrate plugins and SDKs, creating friction that grew over time, according to Modl.ai founder Christoffer Holmgard. The new solution abandons that model entirely. Instead, it treats the game exactly as a human tester would receive it—delivered on a device that mirrors the end‑user’s hardware.
The process begins with what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, and any other relevant material, then converse with the AI using natural‑language prompts. “We simply chat with the system about our game, supplementing the dialogue with images or design docs,” Holmgard says. This conversational interface enables the AI to construct a contextual understanding of the game’s mechanics, narrative, and visual style. Once the AI has a baseline comprehension, developers specify the testing objectives.
These can range from traditional test cases—such as verifying menu navigation or collision detection—to novel scenarios crafted specifically for AI interpretation. Holmgard notes that human expertise is still crucial at this stage: “You need to shape the prompts and language so the AI knows exactly what to look for.” During a test run, the AI watches the game play out on screen, simulating key inputs and recording every visual and data point it can capture. It then hands the raw output to a second AI layer that scans for anomalies, flags potential bugs, and escalates findings to a human QA specialist. The specialist receives a comprehensive bug report that includes textual descriptions, short video clips as evidence, and detailed log files.
“The human reviewer decides what to do with each issue and feeds that decision back into the system,” Holmgard explains, emphasizing the feedback loop that helps the AI differentiate between intentional features and unintended defects. One of the most challenging aspects of automated testing is teaching a machine to recognise the subtle line between a bug and a deliberate design choice. Over time, as QA testers label reports as “bug,” “not a bug,” or “acceptable deviation,” the AI refines its internal model, becoming more attuned to the nuances of each title. Side envisions this technology reshaping the career trajectory of its QA workforce.
Rather than remaining in repetitive, low‑value testing roles, staff can evolve into AI engineers who design prompts, interpret AI‑generated insights, and oversee the integration of machine learning into the testing pipeline. McBride argues that this shift not only raises salaries and creates new career pathways but also makes it possible to bring jobs back to regions where they have previously migrated due to cost pressures. “When you move people up the value chain, you can reinvest in markets that lost jobs because testing was cheap elsewhere,” he says. The AI tool also promises to make the day‑to‑day work of testers more rewarding.
By automating mundane smoke tests and routine verifications for each new build, testers can focus on strategic analysis, gameplay assessment, and creative exploration. “The repetitive load is lifted, allowing our people to think more strategically and grow their expertise,” McBride adds. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “Tedious, repeatable flows don’t need a human to run them over and over.
Those humans can instead evaluate the game experience or conduct more inventive testing, which is where real value lies.” Both executives stress that AI is not a replacement for human QA but a catalyst for upskilling. McBride notes that testing costs remain low in certain countries, making a complete AI takeover impractical.
However, by equipping testers with sophisticated AI tools, Side can extend careers, retain talent, and improve overall quality. “There will always be a human in the loop—that’s our ethos,” he affirms. “The AI reduces the tedious workload and human error, raising the whole process a notch.” When asked about the types of games the system can currently handle, COO Oliver Deslandes is candid: the technology is best suited for simpler titles—puzzle games, card games, or any experience with repeatable mechanics and modest visual complexity. Fast‑paced 3‑D open‑world or fighting games remain on the roadmap for future development.
“We’re focusing on clients willing to embark on this journey with us, even if they have modest expectations at first,” Deslandes says. “Pilots will show where the tech can go.” In summary, the Side‑Modl.ai partnership aims to create a hybrid QA model where AI handles the heavy lifting of data collection and preliminary bug detection, while seasoned human testers provide the critical judgment and contextual insight that only experience can deliver.
By treating AI as a sliding scale rather than a binary replacement, the collaboration hopes to boost efficiency, elevate career prospects, and ultimately deliver higher‑quality gaming experiences to players worldwide.