Side, a seasoned services provider, 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 platform. The partnership merges Side’s deep experience in testing services with Modl.ai’s cutting‑edge machine‑learning capabilities, aiming to create a solution that augments, rather than replaces, human QA professionals. Side, previously known as PTW, has long been a fixture in the outsourcing and testing arena.
Modl.ai, by contrast, is a relatively new player in the gaming sector. Founded in 2018, the startup secured $1.7 million in seed financing two years later and has since focused on applying AI to the challenges of game testing. Martin McBride, Side’s chief commercial officer, described the agreement as “bringing together the best of Side and the best of Modl.ai in a unique offering.” He added that the industry’s “holy‑grail” is an AI‑enhanced QA system that genuinely lifts the value of testing services, and that Side believes it is close to achieving that goal.
Earlier AI‑based QA tools, including Modl.ai’s own first‑generation product, required developers to ship entire builds—or even source code—into a testing environment. That approach relied on integration plugins and SDKs, creating friction and scalability problems, according to Modl.ai founder Christoffer Holmgard.
The new system eliminates the need for any internal access. "We take the product exactly as you would hand it to a QA tester or eventually to the consumer," Holmgard explains.
"You load it onto a comparable device, our solution runs the game by watching the screen, simulating inputs, collecting a comprehensive data set, and then passes that to a second AI layer that flags bugs, escalates them to a human reviewer, and asks whether they merit action." The first phase of the workflow is what Modl.ai calls building "game knowledge." Developers upload assets, design documents, screenshots, and other materials, then converse with the AI in natural language to teach it the specifics of their title. "All of this is in plain English," Holmgard notes. "We chat with the system about the game, feed it images, design docs, and so forth." Once the AI has a baseline understanding, developers specify the test objectives. These can range from traditional test cases to novel scenarios that are better suited to AI‑driven exploration.
Holmgard stresses that human expertise is still essential at this stage: "You need to shape the prompts and language so the AI agent does the right thing." During a test run—referred to as a "session" by Modl.ai—the system records every interaction, producing textual bug reports, short video clips as evidence, and extensive log data. This package is handed to a human QA specialist for review. "The specialist decides what to do with each report for the rest of the production pipeline," Holmgard says.
One of the trickier challenges for AI is distinguishing a genuine bug from an intentional feature. Feedback loops from human testers help the AI refine its judgment. "Some of the learning comes from the prompting, some from the pre‑built game knowledge, and a large part comes from ongoing human feedback on bug reports," Holmgard explains. Over time, the system becomes attuned to the nuances of each game’s design.
Side envisions a future where QA staff evolve into AI engineers, shifting the talent pool up the value chain. McBride argues that this transition can revitalize jobs that have migrated offshore due to low testing wages. "When we move people up the value chain, we can bring them back to regions where they previously disappeared," he says. The AI tool is intended to automate repetitive, low‑skill tasks, freeing testers to focus on strategic analysis and creative problem‑solving.
"One of the biggest benefits right now is that the tool reduces the burden of repetitive tests," McBride adds. "Every new build requires smoke tests and countless verifications that can bog down a tester’s workflow.
By automating those, our staff can think more strategically, engage in deeper analysis, and enjoy a more fulfilling career." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "There are very tedious tests that humans repeat over and over. An AI can handle those flows, allowing people to concentrate on assessing gameplay or performing more inventive testing. That’s the core value proposition." Both leaders stress that AI will not replace QA workers but will augment them.
McBride notes that testing costs in certain low‑wage regions are so low that a complete AI takeover is unrealistic. Instead, providing advanced toolsets enables employees to extend their careers and advance into higher‑paid roles. Side’s philosophy frames AI as a "sliding scale" where a human is always present in the loop.
"That’s our ethos," McBride says. "The tool lets us capture the best of AI—speed, consistency, error reduction—while still relying on human judgment for the nuanced decisions that machines can’t yet master." 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—rather than fast‑paced 3D open‑world or fighting games. "We’re focusing on titles where the mechanics are predictable and the pace isn’t extreme," he explains.
"Clients who are willing to pilot the technology with us will help shape its evolution, and over time we expect to broaden the range of supported games." In summary, the Side‑Modl.ai partnership seeks to blend AI efficiency with human insight, creating a QA ecosystem that automates routine checks while preserving the critical role of expert reviewers. By doing so, they aim to raise the professional ceiling for QA staff, reduce repetitive workload, and ultimately deliver higher‑quality gaming experiences.