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 develop an AI‑powered quality assurance (QA) platform that blends Side’s deep testing experience with Modl.ai’s cutting‑edge machine‑learning capabilities.
### The Partners Side has long been a mainstay in the outsourced services arena, offering a broad suite of testing, localisation, and support solutions to game developers worldwide. Modl.ai, by contrast, is a relative newcomer to the gaming sector. Founded in 2018, the startup secured $1.7 million in seed capital two years later and has since focused on building AI tools that can automate repetitive QA tasks.
"The MOU essentially fuses the strongest assets of both companies into a single, differentiated offering," explains Martin McBride, Side’s chief commercial officer. "One of the holy‑grail ambitions for the games industry today is to leverage AI to augment QA in a meaningful way. No one has cracked that problem yet, and we believe we have a viable path forward." ### From Integration‑Heavy to Plug‑and‑Play Earlier AI‑driven QA solutions—some of which were also created by Modl.ai—required developers to ship entire builds, source code, and even SDK plugins to the testing firm. This "integration‑based" model introduced considerable friction, as developers had to expose internal assets and manage complex hand‑offs.
Modl.ai’s founder, Christoffer Holmgard, acknowledges that the approach became cumbersome over time. The new system eliminates the need for deep integration. "We treat the product exactly as you would hand it to a human tester or eventually to a consumer," Holmgard says.
"The game runs on a device that mirrors the target hardware, our AI watches the screen, simulates key inputs, records every observable event, and then hands the data to a second AI layer that flags anomalies, escalates them to a human reviewer, and asks whether action is required." ### Building "Game Knowledge" The first phase of the workflow involves constructing what Modl.ai calls "game knowledge." Developers upload assets—textures, models, design documents, even gameplay videos—and converse with the AI using natural language. "You can chat with the system about your game, drop in screenshots, feed in design specs, and the AI gradually builds a semantic model of your title," Holmgard explains. Once the AI has a baseline understanding, developers specify the testing objectives.
These can range from classic regression test cases to novel scenarios that are uniquely suited to AI‑driven exploration. "Here you still need human expertise to craft prompts and guide the AI so it performs the right actions," Holmgard notes.
The AI then executes the defined sessions—referred to as "runs"—and compiles a comprehensive bug report. ### Human‑In‑The‑Loop Review Each run generates textual summaries, short video clips (the "evidence"), and extensive log data.
This package is delivered to a seasoned QA specialist who validates the findings, decides on severity, and determines the next steps in the production pipeline. "Distinguishing a genuine bug from an intended feature can be subtle, and human feedback helps the AI learn those nuances over time," Holmgard says.
Feedback loops—both from prompting and from post‑run reviewer comments—gradually fine‑tune the system’s accuracy. ### Upskilling QA Teams Side envisions a future where traditional testers evolve into AI engineers. "Our people can move up the value chain," says McBride.
"Instead of a career path that ends at senior tester, they can become AI‑tool specialists, command higher salaries, and open new career trajectories. This also gives us the chance to repatriate jobs to regions where low‑cost testing has previously migrated away." McBride adds that AI relieves testers from repetitive, low‑skill chores, allowing them to focus on strategic analysis and creative testing. "Smoke tests, routine verifications, and other monotonous tasks are now automated, freeing our staff to think more critically about gameplay balance, user experience, and emergent bugs," he explains.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "Many current tests are tedious and can be run by a machine. That frees humans to assess the fun factor, explore edge cases, and apply their intuition—activities that truly add value." ### AI as an Assist, Not a Replacement Both executives stress that AI will not replace QA professionals. The price point for manual testing in certain low‑cost regions remains so low that a complete AI substitution is economically infeasible. Instead, AI serves as a force‑multiplier, extending the capabilities of human testers and lengthening their careers.
"We’re giving our people tools that let them climb the value chain, stay longer with us, and deliver higher‑impact work," McBride says. Side’s philosophy of a "sliding scale" for AI underscores this balance: there will always be a human in the loop. "That’s our ethos," McBride affirms.
"The AI handles the tedious, error‑prone parts, reducing mistakes and boosting overall quality." ### Current Scope and Future Roadmap When asked about the types of games the solution can handle today, COO Oliver Deslandes admits the focus is on 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 targeting clients willing to experiment with pilots, because the technology’s maturity varies by genre and complexity," he says. Deslandes emphasizes that the journey is iterative. Early adopters will help shape the platform, providing real‑world data that refines AI models and expands applicability. "If you join us now and run pilots, you’ll see firsthand how the tech evolves and where it can take your QA processes," he concludes.
In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles high‑volume, repetitive testing while seasoned humans provide strategic oversight, nuanced judgment, and continuous feedback. This collaborative model promises to raise the bar for game quality, empower QA professionals with new skill sets, and gradually broaden the range of titles that can benefit from intelligent, automated testing.