Side, a veteran services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai to co‑develop an AI‑driven quality‑assurance (QA) platform for video games. The partnership blends Side’s extensive testing pedigree with Modl.ai’s cutting‑edge machine‑learning capabilities, aiming to create a solution that augments, rather than replaces, human QA talent.
Modl.ai, founded in 2018, raised $1.7 million in seed capital two years later and has been building AI tools for the gaming sector ever since. Early versions of its technology required developers to ship entire builds—or even source code—to the QA provider, where a plugin‑based integration would analyze the game. That approach proved cumbersome and, as co‑founder Christoffer Holmgard admits, generated too much friction for widespread adoption.
The new system eliminates the need for deep internal access. "We treat the product exactly as you would hand it to a QA tester or eventually to a consumer," Holmgard explains. "You load it onto a device that mirrors the target hardware, and our AI watches the screen, simulates key inputs, records every observable event, and then passes that data to a second AI layer that flags potential bugs.
Those findings are then reviewed by a human specialist who decides 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, and other relevant material, then converse with the AI in plain language to teach it the fundamentals of their title. "All interactions are natural‑language based," Holmgard says.
"We can ingest images, design docs, even short video clips, so the model forms a holistic understanding of the game world." Once the AI has a baseline comprehension, developers specify the test scenarios they want to run. These can range from traditional test cases—such as verifying menu navigation or level loading—to novel, AI‑friendly challenges that exploit the system’s pattern‑recognition strengths.
"Human expertise is still crucial at this stage," Holmgard notes. "You need to craft prompts and guide the AI so it performs the right actions." During a "run" (Modl.ai’s term for a testing session), the system captures textual logs, video snippets that serve as evidence, and comprehensive telemetry. This package is handed off to a human QA analyst, who reviews the findings, confirms true defects, and annotates any false positives. "The AI can point out anomalies, but a seasoned tester decides whether it’s a bug, a feature, or an intentional design quirk," Holmgard adds.
Feedback from these reviews is fed back into the model, gradually refining its judgment. One persistent challenge in automated testing is distinguishing a bug from an intentional game mechanic.
The continuous loop of human feedback helps the AI learn the nuanced language of game design, improving its accuracy over time. "Prompt engineering, pre‑built game knowledge, and iterative tester feedback all combine to tune the system," Holmgard explains.
Side sees the partnership as a pathway for its QA staff to evolve into AI engineers. Martin McBride, Side’s chief commercial officer, argues that the technology lifts employees up the value chain: instead of spending hours on repetitive smoke tests, they can focus on strategic analysis, creative testing, and AI model training. This shift not only enhances job satisfaction but also opens higher‑pay roles, potentially repatriating jobs to regions where low‑cost testing has previously migrated.
"By automating the drudgery, we free our people to think strategically and grow their careers," McBride says. "They become AI engineers, earn more, and we can bring talent back to markets that lost testing jobs due to price pressures." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: many current tests are monotonous and can be performed more efficiently by a machine. "When the AI handles repetitive flows, human testers can devote their expertise to assessing gameplay feel, exploring edge cases, and conducting more inventive testing.
That’s the real value proposition of AI in QA." Both leaders stress that AI will never fully replace human testers. The price point for low‑cost testing in certain countries remains too low to justify a complete automation takeover. Instead, the AI acts as a force multiplier, enabling testers to handle more complex, higher‑value tasks while maintaining a safety net of human oversight.
Side describes its AI philosophy as a "sliding scale," where the degree of automation can be adjusted based on project needs, but a human will always remain in the loop. "That’s our ethos," McBride affirms.
"The tool amplifies what AI can do, reduces repetitive workload, and cuts down human error, but the final judgment always rests with a person." Regarding applicability, Side’s chief operating officer Oliver Deslandes cautions that the current solution 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. "We’re focusing on projects where the AI can deliver immediate value while we continue to refine the technology for more demanding genres," Deslandes says. He adds that early adopters willing to pilot the system will help shape its evolution and demonstrate concrete cost‑savings. In summary, the Side‑Modl.ai collaboration aims to blend AI’s speed and consistency with human intuition and expertise, creating a hybrid QA model that enhances efficiency, reduces error, and opens new career pathways for testers.
The partnership reflects a broader industry trend: leveraging machine learning to handle routine validation while preserving the creative and critical thinking that only seasoned humans can provide.