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. The agreement sets the stage for a joint venture that will blend Side’s extensive QA expertise with Modl.ai’s cutting‑edge AI technology to create a new, AI‑enhanced quality‑assurance platform for the gaming industry. Modl.ai, although a relatively recent entrant to the market, has quickly built a reputation since its 2018 founding.
After securing $1.7 million in seed funding in 2020, the company has focused on developing machine‑learning tools that can automate parts of the testing process. Martin McBride, Side’s chief commercial officer, describes the partnership as “the convergence of Side’s deep QA know‑how and Modl.ai’s AI capabilities into a unique, market‑changing solution.” He adds that the industry has long sought a reliable way to use AI to augment QA, and that this collaboration could finally deliver on that promise.
Historically, AI‑driven QA solutions required developers to hand over entire game builds—or even source code—to the testing provider. Modl.ai’s earlier offerings relied on a plugin and software development kit (SDK) that needed deep integration with the game’s internals, a process that developers found cumbersome and that often stalled over time, according to founder Christoffer Holmgard. The new approach eliminates that friction. Holmgard explains that the system treats the game exactly as a human tester would receive it: a compiled build installed on a device that mirrors the end‑user environment.
The AI then runs the game, watches the screen, simulates inputs, records every interaction, and passes the resulting data to a second AI layer that flags potential bugs. Those flagged items are then reviewed by a human specialist, who decides whether they merit further action. The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets—artwork, design documents, level layouts—and engage in a natural‑language dialogue with the AI to teach it the nuances of their title. "All of this happens in plain English," Holmgard notes, "we can chat, share images, feed design docs, and the AI gradually builds a mental model of the game." Once the AI has a solid understanding, the development team outlines the testing objectives.
These can range from traditional test cases to novel scenarios specifically crafted to leverage the AI’s strengths. Holmgard stresses that human insight remains essential at this stage: "You need experienced testers to phrase prompts correctly and guide the AI toward the right actions." During a test run—referred to by Modl.ai as a “session”—the system captures a wealth of evidence: textual logs, short video clips, and comprehensive telemetry. This package is handed over to a human QA analyst who reviews the findings, validates true defects, and discards false positives. The analyst’s feedback is fed back into the AI, helping it refine its judgment over time.
Holmgard highlights that distinguishing a bug from an intentional feature is a subtle challenge, and continuous human input is the key to teaching the AI these distinctions. Side envisions this collaboration reshaping the career trajectory of its QA workforce. Rather than remaining confined to repetitive test execution, staff can evolve into AI engineers who design prompts, interpret model outputs, and fine‑tune algorithms.
McBride explains that this shift “moves our people up the value chain,” offering higher salaries, new career paths, and the possibility of bringing jobs back to regions that have lost testing work due to cost pressures. By automating monotonous tasks, the technology frees testers to focus on strategic analysis, creative problem‑solving, and higher‑order quality assessments. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that many current tests are mind‑numbing loops that a machine can execute far more efficiently. "When the AI handles the rote flows, human testers can concentrate on evaluating gameplay feel, exploring edge cases, and performing more inventive testing," she says, framing the AI as a productivity enhancer rather than a replacement.
Both companies stress that AI will never fully supplant human QA talent. McBride points out that testing costs remain low in certain markets, making a total automation strategy impractical. Instead, the AI acts as a tool that amplifies human capability, reduces error rates, and streamlines workflow. He describes the AI adoption model as a “sliding scale” where the proportion of automation can be adjusted, but a human will always remain in the loop—a core part of Side’s ethos.
When asked about the types of games the system currently supports, COO Oliver Deslandes is candid: the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, graphically intensive 3D open‑world or fighting games are still on the roadmap but require further development. Deslandes emphasizes that early adopters should be willing to experiment and accept that the solution will evolve alongside their needs.
In summary, the Side‑Modl.ai partnership aims to blend seasoned QA practice with AI automation to create a more efficient, less error‑prone testing pipeline. By preserving a human‑in‑the‑loop approach, the collaboration seeks to enhance job quality, open new technical career avenues, and ultimately deliver higher‑quality games to players worldwide.
The six‑month MOU marks the beginning of a pilot phase that will test the system on real‑world projects, gather feedback, and refine the technology for broader deployment in the future.