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 effort to create an AI‑augmented quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning technology. Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later.
While the company has already experimented with AI‑powered QA tools, earlier versions required developers to hand over full game builds, source code, or integrate a proprietary SDK. This approach proved cumbersome and, as Modl.ai’s founder Christoffer Holmgard admits, “became difficult over time.” The new solution, however, eliminates the need for any internal access to the game’s code base.
Holmgard explains that the product is treated exactly as a human tester would receive it: it is installed on a device that mirrors the end‑user’s hardware, then the AI runs the game, observes the screen, simulates inputs, and records every interaction. A second AI layer analyses the generated data, flags potential defects, and escalates them to a human reviewer for final validation.
The process begins with what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, or any other relevant material and converse with the AI in plain language. This conversational interface allows the system to develop a contextual understanding of the game’s mechanics, narrative, and visual style.
Holmgard notes that the interaction is entirely natural‑language based, with the option to enrich the dialogue with images or other media. Once the AI has a solid grasp of the title, developers specify the testing objectives. These can range from traditional test cases—such as verifying UI elements or ensuring level‑loading stability—to more exploratory scenarios that are specifically crafted to leverage the AI’s strengths. Holmgard stresses that human expertise is still required at this stage to phrase prompts correctly and guide the AI toward the most valuable investigations.
During a testing session, which Modl.ai refers to as a “run,” the system produces a comprehensive bug report. The output includes textual descriptions, short video clips that serve as evidence, and detailed log files.
These artifacts are then handed over to a seasoned QA specialist who reviews the findings, decides on their relevance, and determines the next steps in the production pipeline. One of the persistent challenges in automated testing is distinguishing between a genuine defect and an intentional feature.
Holmgard explains that the AI improves its judgment over time through a feedback loop: testers label each report as a bug, a non‑bug, or a design‑intended behavior, and the system incorporates this information to refine its future assessments. This iterative learning process gradually attunes the AI to the subtle nuances of game design. Side envisions a broader transformation of the QA workforce as a result of this partnership.
According to Martin McBride, Side’s chief commercial officer, the integration of AI will enable QA staff to evolve into “AI engineers.” Rather than spending the bulk of their day executing repetitive smoke tests, employees will shift toward higher‑value activities such as strategic analysis, test‑case design, and AI‑model supervision. McBride believes this shift can raise salaries, create new career pathways, and even bring jobs back to regions where low‑cost testing has previously been outsourced. Both McBride and Modl.ai’s chief product officer Benedikte Mikkelsen agree that AI is not intended to replace human testers but to relieve them of monotonous tasks.
Mikkelsen points out that many current tests involve running the same flow repeatedly—a scenario perfectly suited for automation. By offloading these chores, testers can focus on more creative pursuits, such as evaluating gameplay feel, exploring emergent mechanics, or conducting exploratory testing that requires human intuition. The partnership also underscores a philosophical stance on AI adoption.
McBride describes AI as a “sliding scale,” insisting that a human will always remain in the loop. This ethos reflects Side’s commitment to augmenting, rather than supplanting, human expertise.
He argues that removing the tedium of repetitive checks reduces error rates and allows the team to operate at a higher level of precision and insight. Regarding the types of games the solution currently supports, Side’s chief operating officer Oliver Deslandes notes that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics.
Fast‑paced 3D open‑world or fighting games present a more complex challenge and are slated for later development. Deslandes emphasizes that early adopters should be willing to participate in pilot projects and understand that the technology is still maturing. In summary, the Side‑Modl.ai collaboration aims to blend the strengths of seasoned QA professionals with AI’s capacity for rapid, data‑driven analysis. By constructing a workflow that starts with natural‑language game knowledge, proceeds through AI‑guided testing, and culminates in human‑validated bug reports, the partnership hopes to deliver a more efficient, accurate, and fulfilling QA process.
While the system is presently geared toward less intricate games, the roadmap includes expanding to more demanding titles as the AI models evolve. Ultimately, the goal is to create a symbiotic environment where AI handles the repetitive grunt work, human experts provide strategic oversight, and both parties benefit from increased productivity and higher‑quality game releases.