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‑powered quality‑assurance platform for video games. The agreement binds the two companies to collaborate on a system that blends Side’s extensive QA expertise with Modl.ai’s cutting‑edge machine‑learning technology, aiming to create a more efficient and scalable testing process while retaining human judgment at critical points. Side, formerly known as PTW, has been a fixture in the outsourcing and testing arena for many years. Modl.ai, by contrast, is a relatively new entrant to the gaming sector; founded in 2018, it secured $1.7 million in seed capital two years later and has since focused on applying AI to the challenges of game testing.
Martin McBride, Side’s chief commercial officer, describes the partnership as a marriage of complementary strengths. “The MOU brings together the best of Side’s QA heritage and Modl.ai’s AI know‑how into a unique offering,” he says. “One of the holy‑grail ambitions for the industry is to use AI to augment QA services in a way that truly adds value. We believe we’re on the cusp of that breakthrough.” Earlier AI‑driven QA attempts, including earlier versions of Modl.ai’s own tools, required developers to hand over full builds or even source code to the testing provider.
Those solutions relied on deep integration via plugins and SDKs, creating friction and limiting adoption. “It became cumbersome over time,” admits Modl.ai founder Christoffer Holmgard. The new approach discards the need for internal access.
Instead, developers supply the game exactly as they would to a human tester—installed on a comparable device. The AI then runs the game, observes the screen, simulates inputs, and compiles a comprehensive data set of every event.
A second AI layer analyses that data, flags potential bugs, and escalates the findings to a human reviewer for final validation. The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, or even converse with the system in natural language to teach the AI about the game’s mechanics, narrative, and visual style.
“All of this happens through plain conversation,” Holmgard explains. “You can chat with the AI, feed it images, or drop in design docs, and it gradually builds an internal model of your title.” Once the AI has a solid understanding, the development team defines the test objectives. These can range from conventional test cases—such as checking UI navigation or collision detection—to more exploratory scenarios that are specifically crafted to leverage the AI’s pattern‑recognition capabilities.
Holmgard notes that human expertise remains essential at this stage: “You need to guide the prompts and language so the AI knows exactly what to look for.” During a test run, the system records logs, captures short video clips as evidence, and generates textual bug reports. These artifacts are handed to a seasoned QA specialist who reviews the findings, decides which issues merit further action, and provides feedback to the AI. “The AI learns from this loop,” Holmgard says.
“When testers label a finding as a bug, not a bug, or a design choice, the model refines its judgment over time.” This human‑in‑the‑loop approach helps the AI distinguish between genuine defects and intentional features, a nuance that pure automation often misses. Side envisions a broader shift in career trajectories for its QA staff. By equipping testers with AI tools, the company hopes to move them up the value chain from repetitive test execution to roles akin to AI engineers or data analysts.
“Instead of a linear path from junior tester to senior tester, we can open pathways toward AI engineering,” McBride says. “That not only raises salaries but also creates new career ladders and potentially brings jobs back to regions where low‑cost testing was previously outsourced.” The partnership also promises a more satisfying work environment. Automating monotonous smoke tests and regression checks frees testers to focus on strategic analysis, creative test design, and deeper gameplay evaluation.
“Our tool reduces the burden of repetitive tests, allowing people to think strategically and grow professionally,” McBride adds. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “There are many tedious flows that a human doesn’t need to repeat. By offloading those, testers can spend time on higher‑value activities like assessing game feel or crafting innovative test scenarios.” Despite the optimism, both sides stress that AI will not replace QA professionals. The price point for low‑cost testing in certain markets remains so low that a complete AI takeover is neither feasible nor desirable.
Instead, the technology is viewed as a lever to elevate human workers, extending their careers and enhancing productivity. “There will always be a human in the loop—that’s our ethos,” McBride affirms. “AI helps us cut errors and fatigue, but the final judgment stays with people.” Regarding applicability, Side’s chief operating officer Oliver Deslandes notes that the current solution is best suited for simpler titles—puzzle games, card games, or other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world adventures or fighting games.
“We’re focusing on games where the AI can reliably interpret screen output and input patterns,” he says. “Clients willing to pilot this technology now will help us refine it for more complex genres in the future.” In summary, the Side‑Modl.ai collaboration seeks to blend AI’s speed and consistency with human insight and creativity, aiming to transform game QA from a labor‑intensive chore into a more strategic, rewarding discipline.
The six‑month MOU marks the start of a journey that could reshape testing workflows, career paths, and the global distribution of QA talent as the technology matures.