Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence specialist Modl.ai. The partnership is designed to create a hybrid, AI‑driven quality‑assurance (QA) platform that blends the strengths of both companies into a single, more efficient solution for the gaming industry. Modl.ai, a relatively new player in the games market, was founded in 2018 and secured $1.7 million in seed financing two years later.
Side, by contrast, has a long‑standing reputation for delivering outsourced services across a range of sectors. According to Side’s chief commercial officer Martin McBride, the MOU “effectively brings the best of what Side has and the best of what Modl.ai has together in a unique solution.” He adds that the collaboration aims to deliver what many in the industry have called a "holy grail"—an AI‑enhanced QA system that truly augments human testers rather than merely automating rote tasks. Historically, AI‑powered QA tools required developers to hand over entire game builds or even source code to the testing vendor.
Modl.ai’s earlier offering relied on an integration model that used a plug‑in and software development kit (SDK), demanding deep access to a game’s internal assets. This approach proved cumbersome and, as Modl.ai founder Christoffer Holmgard explains, “became difficult over time.” The new system eliminates the need for such invasive access.
Instead, developers provide the product exactly as they would to a traditional QA tester—or ultimately, to the end‑user. The game runs on a device that mirrors the consumer’s hardware, while the AI watches the screen, simulates inputs, records every observable event, and then passes the resulting data to a second AI layer that flags potential bugs and escalates them for human review. The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, and other relevant materials, then converse with the AI in natural language to teach it the specifics of the title. Holmgard notes that the interaction is entirely conversational: “We just chat with the system about our game, but we’re also adding in images, or we might upload our game design documents and so on.” This creates a contextual foundation that the AI can draw upon when later generating test scenarios.
Once the AI has a solid understanding of the game, developers specify the testing objectives. These can range from classic test cases—such as verifying menu navigation or level loading—to novel, AI‑friendly scenarios that exploit the system’s pattern‑recognition abilities. Holmgard stresses that human expertise is still essential at this stage: “You need some human expertise here to adapt the prompting and the language to the AI agent to make it do the right things.” During a test run, the AI records all on‑screen activity, logs system events, and captures short video snippets that serve as evidence of any anomalies.
The output includes a structured bug report, complete with textual descriptions, video clips, and raw logs. These reports are then handed to a human QA specialist, who validates the findings and decides on the appropriate corrective action. Holmgard points out a key challenge: distinguishing a genuine bug from an intentional game feature. “Feedback from QA testers will, in part, help the AI system understand the nuances of game design,” he says.
Over time, as testers label reports as bugs, non‑bugs, or edge‑case features, the AI refines its judgment and becomes more accurate. Side sees this collaboration as a pathway to upskill its workforce.
Rather than remaining in low‑margin, repetitive testing roles, employees can transition into AI‑engineer positions, overseeing prompt engineering, data curation, and model supervision. McBride explains that this shift “allows our people to get paid more money…to have new career paths…and potentially bring jobs back to countries where they historically have left because of price‑point pressures.” By automating the most monotonous tests—such as smoke tests for each new build—human testers can focus on strategic analysis, gameplay assessment, and creative testing approaches. “When you knock out the repetitive work, testers get to think more strategically and grow their careers,” McBride adds. Modl.ai’s chief product officer Benedikte Mikkelsen echoes this sentiment, noting that many current tests are “very tedious” and could be handled by an algorithm, freeing humans to evaluate the gameplay experience itself.
Both companies emphasize that AI is not a replacement for QA professionals but a tool that elevates them along the value chain. The technology is not yet suited for every type of game. Side’s chief operating officer Oliver Deslandes admits that the solution works best with simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games.
“We have to focus on titles that are more appropriate,” he says, adding that early adopters will likely be studios willing to experiment and accept the learning curve. The partnership is positioned as a pilot phase, with the expectation that the system will evolve as more data is collected and the AI models are refined.
In summary, the Side‑Modl.ai alliance aims to deliver a hybrid QA platform that blends AI efficiency with human judgment. By providing a non‑intrusive testing method, building contextual game knowledge through natural‑language interaction, and keeping humans in the decision loop, the collaboration seeks to reduce repetitive workload, improve bug detection accuracy, and create new, higher‑value roles for QA staff.
The six‑month MOU serves as a testbed for these ideas, and both parties view the journey as iterative: as the technology matures, it could eventually support more complex game genres and broaden its impact across the global gaming ecosystem.