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 build a quality‑assurance (QA) platform that leverages AI while keeping human expertise at its core.
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. Side brings decades of experience in delivering outsourced services to a variety of industries, including video‑game development. Together, the two companies aim to combine their respective strengths into a single, differentiated offering.
Martin McBride, Side’s chief commercial officer, describes the partnership as “the marriage of Side’s deep service knowledge with Modl.ai’s cutting‑edge AI capabilities.” He adds that the gaming industry has long sought a reliable way to augment QA with machine intelligence, and that the duo believes they have finally found a workable solution. Earlier AI‑driven QA tools required developers to ship full builds or even share source code with the testing provider.
Modl.ai’s previous approach relied on a plugin and software‑development kit (SDK) that developers had to integrate into their pipelines, a process that proved cumbersome and, as founder Christoffer Holmgard notes, “became difficult over time.” The new system eliminates that friction. Holmgard explains that the AI treats the product exactly as a human tester would receive it—by loading the game onto a device that mirrors the end‑user’s hardware. The AI then plays the title, watches the screen, simulates inputs, records every observable event, and passes the resulting data set to a second AI layer that flags potential bugs. Those flagged items are then presented to a human QA specialist for verification and triage.
The workflow 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 English (or other natural languages). This dialogue helps the system construct an internal model of the game’s mechanics, narrative, and visual style.
Once the AI has a sufficient understanding, developers specify the testing objectives—ranging from classic regression cases to novel scenarios that are especially suited to AI‑driven exploration. Holmgard emphasizes that human input remains essential at this stage. “You need expertise to shape the prompts and guide the AI so it performs the right actions,” he says.
After the AI runs a “session” (Modl.ai’s term for a test run), it compiles a bug report that includes textual descriptions, short video clips as evidence, and comprehensive log data. This package is handed to an experienced QA analyst, who decides whether the issue warrants further action.
One of the trickier challenges for any automated system is distinguishing a genuine defect from an intentional feature. Holmgard notes that continuous feedback from human testers helps the AI refine its judgment. Over time, as testers label findings as bugs, non‑bugs, or edge‑case features, the AI’s model becomes more attuned to the nuances of game design. Side envisions a broader transformation for its workforce.
By equipping QA staff with AI tools, the company hopes to shift many employees from routine testing roles into “AI engineering” positions. McBride explains that this transition not only raises salaries and creates new career pathways but also makes it possible to repatriate jobs that have migrated to lower‑cost regions. When testers move up the value chain, they can remain in higher‑wage markets while still contributing to the same projects.
According to McBride, AI should free testers from repetitive, low‑skill tasks such as smoke testing every new build. By automating those chores, QA professionals can focus on strategic analysis, deeper gameplay evaluation, and creative testing approaches—activities that are more rewarding and better aligned with career growth.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment. She points out that many current tests are “tedious” and can be performed more efficiently by machines, allowing humans to concentrate on assessing gameplay quality and devising innovative test scenarios. This, she argues, is the primary value proposition of integrating AI into QA.
Both leaders stress that AI will never fully replace human QA workers. The price of manual testing in certain low‑cost regions remains so low that a complete AI substitution would be economically infeasible.
Instead, the technology is intended to augment human effort, extending careers and enhancing job satisfaction. McBride sums up Side’s philosophy as a “sliding scale” of AI involvement, insisting that “there will always be a human in the loop.” This ethos underpins the partnership’s goal of combining AI efficiency with human judgment to reduce errors, cut down repetitive workload, and ultimately raise the overall quality of game releases. When asked about the types of games the system can handle, Chief Operating Officer Oliver Deslandes is candid: the current iteration works best with simpler titles—puzzle games, card games, and other experiences with repeatable mechanics.
Fast‑paced, highly complex 3D open‑world or fighting games remain a longer‑term target. Deslandes stresses that early adopters need to be willing to experiment and accept that the technology is still evolving. He concludes that the journey is ongoing, but partners who engage now will witness the platform’s progression firsthand.