Side, a long‑standing 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 agreement sets the stage for a joint effort to build an AI‑driven quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities.
Modl.ai, although a newer name in the gaming ecosystem, has quickly gained traction since its 2018 launch, securing $1.7 million in seed capital two years later. The partnership is framed by Side’s chief commercial officer, Martin McBride, who describes the collaboration as “the best of Side and the best of Modl.ai rolled into a single, unique solution.” He adds that the gaming industry has long sought a “holy‑grail” AI tool that can truly augment QA services, and that the two companies believe they are on the cusp of delivering it. Earlier AI‑enabled QA attempts—including Modl.ai’s own first‑generation product—required developers to ship full builds or even share source code so that the testing engine could hook into the game via plugins and SDKs.
This approach proved cumbersome and generated friction, as noted by Modl.ai founder Christoffer Holmgard, who explained that the process “became difficult over time.” The new system, by contrast, eliminates the need for any internal access. Instead, the game is presented to the AI exactly as a QA tester or end‑user would receive it: installed on a comparable device, run, and observed. The AI watches the screen, simulates player inputs, logs every event, and then hands the resulting data set to a second AI layer that scans for anomalies, flags potential bugs, and escalates them to a human reviewer for final validation.
Holmgard calls the initial phase “building game knowledge.” Developers upload assets, design documents, screenshots, or even converse with the system in natural language to teach the AI the fundamentals of their title. Once the AI has a basic understanding, developers specify the testing objectives.
These can range from traditional test cases to novel scenarios that are particularly well‑suited to machine‑learning analysis. Holmgard stresses that human expertise remains essential at this stage to craft effective prompts and guide the AI’s behavior. The AI then executes a “run” (Modl.ai’s term for a testing session) and produces bug reports that include textual descriptions, short video clips as evidence, and comprehensive log data. Human QA specialists receive these reports, evaluate the findings, and decide how to act.
One persistent challenge for AI is distinguishing a genuine bug from an intentional feature. Feedback loops from human testers help the system refine its judgment. Holmgard explains that the AI learns from three sources: the initial prompting, the pre‑built game knowledge, and ongoing feedback from QA staff who label each report as “bug,” “not a bug,” or “not a bug for this reason.” Over time, the model becomes attuned to the nuances of a particular game’s design language.
Side envisions a broader shift in the QA workforce. As AI takes over repetitive, low‑value tasks, QA professionals can transition into “AI engineers,” a role that commands higher salaries and offers more strategic influence. McBride notes that this upward mobility can revive jobs in regions where testing has migrated away due to cost pressures.
By moving staff up the value chain, Side hopes to retain talent longer and create new career pathways. The impact on day‑to‑day work is also significant.
McBride points out that the AI dramatically reduces the burden of routine smoke tests and verification steps that accompany every new build. With those chores automated, testers can focus on higher‑order analysis, strategic thinking, and creative exploration of gameplay. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, describing the AI as a tool that frees humans from “tedious tests” so they can concentrate on assessing the game experience itself. Both leaders are careful to stress that AI will not replace QA staff.
The price of manual testing in certain low‑cost markets remains so low that a complete AI takeover is economically unrealistic. Instead, the technology is positioned as an enhancer that lifts people’s roles, reduces human error, and improves overall quality. McBride frames AI as a “sliding scale” – a spectrum where human involvement is always present, aligning with Side’s core ethos of keeping a person in the loop. When asked about the types of games the solution currently supports, Side’s chief operating officer Oliver Deslandes admits that the platform is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world adventures or fighting games.
He explains that the technology must first prove itself on manageable projects before tackling more complex, high‑velocity genres. Deslandes encourages early adopters to join pilot programs, emphasizing that the journey is iterative and that the partnership’s success will depend on collaborative experimentation.
In summary, the Side‑Modl.ai MOU represents a strategic effort to fuse human expertise with AI efficiency in game QA. By removing the need for deep code integration, leveraging natural‑language interactions, and creating feedback‑driven learning loops, the joint solution aims to streamline bug detection while preserving the critical role of skilled testers.
The ultimate goal is to elevate QA professionals into more valuable, AI‑centric positions, thereby reshaping the global landscape of quality assurance in the gaming industry.