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 the strengths of both organisations.
Modl.ai, a relatively new entrant to the gaming sector, was founded in 2018 and secured $1.7 million in seed funding two years later. Side brings decades of experience delivering QA services to game developers, while Modl.ai contributes cutting‑edge machine‑learning technology. "The MOU effectively brings the best of what Side has and the best of what Modl.ai has together in a unique solution," says Martin McBride, Side’s chief commercial officer. He adds that the gaming industry has been searching for a "holy grail" – a way to harness AI to augment QA without sacrificing accuracy – and that this partnership may finally deliver that promise.
Earlier AI‑driven QA tools, including Modl.ai’s initial offering, required developers to ship entire builds or even share source code with the testing vendor. That approach relied on deep integration via plugins and SDKs, creating friction and, as co‑founder Christoffer Holmgard explains, "became difficult over time." The new system abandons that model.
Instead, it treats the game exactly as a QA tester or end‑user would receive it – a compiled binary installed on a device that mirrors the target platform. The AI watches the screen, simulates inputs, records every interaction, and then hands the resulting data set to a second AI layer that flags potential bugs. Human reviewers then decide which issues merit action.
The first phase, which Modl.ai calls "building game knowledge," involves developers uploading assets, design documents, screenshots, and other relevant materials. The AI ingests these inputs and learns to speak about the game in natural language.
"All of this is in natural language," Holmgard notes. "We just chat with the system about our game, but we're also adding in images, or we might upload our game design documents and so on." Once the AI has a contextual understanding, developers specify the test scenarios they want to run. These can range from traditional test cases to novel, AI‑friendly challenges that exploit the system’s strengths.
Holmgard stresses that human expertise remains crucial at this stage: "You need some human expertise here to adapt the prompting and the language to the AI agent to make it do the right things." During a test run, the platform captures textual logs, video snippets (referred to as "evidence"), and comprehensive telemetry. This information is compiled into a bug report and passed to a seasoned QA specialist. The specialist reviews the findings, determines whether each item is truly a defect, a feature, or a false positive, and provides feedback that the AI uses to refine its models.
Over time, this iterative loop helps the system distinguish subtle design nuances that might otherwise be misinterpreted. One of the persistent challenges in automated testing is differentiating a bug from an intentional gameplay mechanic. Holmgard explains that the AI learns from three sources: the initial prompting, the pre‑built game knowledge, and continuous feedback from human testers.
"Over time, the system gets attuned," he says. Side envisions a broader impact on the workforce. As AI takes over repetitive, low‑value tasks, QA personnel can transition into "AI engineers" – roles that involve designing prompts, interpreting AI outputs, and fine‑tuning models.
McBride argues that this shift elevates employees up the value chain, offering higher salaries, new career trajectories, and the possibility of bringing jobs back to regions where they have previously migrated due to cost pressures. "Instead of just doing testing and moving up to senior tester, they can become an AI engineer," he says. "It allows our people to get paid more, opens new career paths, and can repatriate jobs to countries where they historically left." The partnership also promises a more fulfilling day‑to‑day experience for QA staff. By automating smoke tests, regression checks, and other routine verifications, testers can focus on strategic analysis, gameplay assessment, and creative testing approaches.
"The tool reduces the burden of repetitive tests, letting people think more strategically and grow their careers," McBride explains. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that many current tests are monotonous and could be better performed by humans evaluating gameplay quality rather than repeating the same flow. Both companies stress that AI will not replace human QA workers but will complement them.
McBride points out that testing costs remain low in certain regions, making a full AI takeover impractical. Instead, providing AI‑assisted toolkits enables staff to extend their expertise and remain valuable contributors for longer periods. Side’s philosophy frames AI as a "sliding scale" with a permanent human‑in‑the‑loop component.
"That's sort of our ethos," McBride says. "The tool lets us take the best of what AI can offer, reduce the burden, and also reduce mistakes. People are prone to human error; removing that variable lifts the overall quality." When asked about the types of games the solution currently supports, COO Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced 3D open‑world or fighting games remain a longer‑term goal.
"We’re focusing on titles that are more appropriate for the current stage," he explains, adding that early adopters will help shape the roadmap as the platform matures. In summary, the Side‑Modl.ai collaboration aims to create a hybrid QA workflow where AI handles the heavy lifting of data collection and initial bug detection, while seasoned human testers provide the critical judgment and contextual insight that only experience can supply. By doing so, the partnership hopes to raise the overall efficiency, accuracy, and career satisfaction within the gaming QA ecosystem.