Side, a long‑standing 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 an AI‑driven quality‑assurance (QA) platform that blends Side’s deep industry experience with Modl.ai’s cutting‑edge technology.
Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed funding two years later. Its chief commercial officer, Martin McBride, describes the partnership as a marriage of the best assets each company brings to the table. He calls AI‑enhanced QA the "holy grail" for the games industry, noting that no one has yet delivered a truly effective solution.
According to McBride, the new system could finally fill that gap. Earlier AI‑based QA tools, including Modl.ai’s previous offering, required developers to hand over full game builds or even source code so the system could run tests. That approach relied on integration via plugins and SDKs, creating friction and, as founder Christoffer Holmgard admits, becoming cumbersome over time.
The upcoming solution eliminates the need for internal access. Instead, the product is treated exactly as a QA tester would receive it – a finished build placed on a device that mirrors the end‑user’s hardware. The AI then watches the screen, simulates key actions, records everything that happens, and passes the resulting data to a second AI layer that flags potential bugs.
Human reviewers then decide whether each issue warrants action. The process begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, and other relevant material, then converse with the AI in natural language to teach it the fundamentals of their title.
Holmgard explains that this dialogue can include text, images, and even video, allowing the system to develop a nuanced understanding of the game’s mechanics and objectives. Once the AI has a baseline comprehension, developers specify the testing scope.
This can range from traditional test cases to novel scenarios that are particularly well‑suited to machine‑learning‑driven exploration. Holmgard stresses that human expertise is still crucial at this stage; testers must craft prompts and language that guide the AI to perform the right actions.
During a test run – referred to as a "session" by Modl.ai – the system generates bug reports that include textual descriptions, short video clips as evidence, and comprehensive log data. These artifacts are handed over to a human QA specialist who reviews them, determines their validity, and decides the next steps for the production pipeline. One of the biggest challenges for an AI is distinguishing a genuine bug from an intentional feature.
Holmgard notes that ongoing feedback from QA testers helps the AI refine its judgment, gradually attuning itself to the subtle design decisions that differentiate a flaw from a purposeful element. Side envisions this technology reshaping the career trajectory of its QA staff. Rather than remaining confined to repetitive testing tasks, employees could evolve into AI engineers, overseeing and fine‑tuning the machine‑learning models that power the system.
McBride argues that this shift would raise the value of the workforce, enable higher salaries, and even allow some jobs to return to regions where they have previously migrated due to cost pressures. By moving staff up the value chain, Side hopes to retain talent longer and create more fulfilling career paths. According to McBride, the AI tool primarily alleviates the burden of monotonous tests such as smoke checks and routine verifications that must be repeated for every new build. Automating these tasks frees testers to focus on strategic analysis, creative problem‑solving, and higher‑level quality work.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, pointing out that many current tests are tediously repetitive and could be handled by a machine, allowing humans to concentrate on assessing gameplay and conducting more inventive testing approaches. Both executives are clear that AI will not replace QA professionals. The price of manual testing in certain low‑cost regions remains so low that a complete automation takeover is unrealistic.
Instead, the technology serves as an augmentation tool that elevates human testers, giving them better tools and longer, more rewarding careers. McBride describes AI as a "sliding scale" – a spectrum where human involvement is always present, aligning with Side’s core ethos of keeping a human in the loop.
When asked about the types of games the solution currently supports, 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. Fast‑paced, complex 3D open‑world or fighting games are still a ways off. Deslandes emphasizes that early adopters will likely be studios willing to experiment and co‑develop the technology, understanding that the journey will involve pilots, iterative improvements, and gradual expansion into more demanding genres.
In summary, the Side‑Modl.ai partnership aims to deliver an AI‑enhanced QA workflow that reduces repetitive workload, improves bug detection accuracy, and creates new professional pathways for QA staff. By combining Side’s industry know‑how with Modl.ai’s innovative AI approach, the collaboration aspires to set a new standard for quality assurance in the gaming sector, while steadfastly maintaining human oversight as a fundamental principle.