Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Denmark‑based artificial‑intelligence specialist Modl.ai to co‑develop an AI‑driven quality‑assurance platform for the gaming sector. The agreement binds the two companies to collaborate on a system that blends Side’s deep QA experience with Modl.ai’s cutting‑edge AI technology, aiming to create a more efficient, human‑in‑the‑loop testing process. Modl.ai, founded in 2018, is a relatively new player in the games industry but has already secured $1.7 million in seed funding. Its chief commercial officer at Side, Martin McBride, describes the partnership as “the best of both worlds,” positioning the joint solution as a potential "holy grail" for game developers seeking to augment traditional QA with intelligent automation.
He notes that while many firms have experimented with AI‑enhanced testing, few have delivered a practical, scalable offering—something Side believes it can now achieve with Modl.ai’s expertise. Earlier AI‑based QA tools required developers to hand over entire game builds, source code, or integrate proprietary plugins and SDKs. This approach created significant friction; developers were reluctant to expose internal assets, and the integration overhead often proved unsustainable. Modl.ai’s founder, Christoffer Holmgard, explains that the new system eliminates the need for deep code access.
Instead, the product is treated like any other QA sample: it is installed on a device that mirrors the end‑user environment, and the AI observes the game’s visual output, simulates inputs, and records a comprehensive data set of on‑screen events. The recorded data is then passed to a secondary AI layer that scans for anomalies, flags potential bugs, and escalates findings to human reviewers.
This two‑stage pipeline begins with 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 nuances of their title. Holmgard emphasizes that the interaction remains conversational—"we just chat with the system about our game, adding images or design docs as needed." Once the AI has a baseline understanding, developers specify test objectives.
These can range from conventional test cases to novel scenarios tailored to the AI’s strengths. Holmgard stresses that human expertise is still essential at this stage to craft effective prompts and guide the AI toward the desired outcomes. After the AI executes the test run, it produces a bug report that includes textual descriptions, short video snippets (referred to as "evidence"), and detailed logs. These artifacts are handed to a seasoned QA specialist who validates the findings and decides the next steps in the production pipeline.
A persistent challenge in automated testing is distinguishing genuine defects from intentional game features. The system relies on feedback loops from human testers to refine its judgment. Over time, as testers label reports as "bug," "not a bug," or provide contextual explanations, the AI’s model becomes increasingly attuned to the specific design language of the game. Side envisions this technology reshaping the career trajectory of its QA staff.
Rather than remaining confined to repetitive test execution, employees can evolve into AI engineers who design prompts, interpret model outputs, and fine‑tune the system. McBride argues that this shift not only elevates wages and job satisfaction but also opens the possibility of bringing QA roles back to regions where low‑cost testing has previously migrated away. By moving workers 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 repetitive tests” such as smoke checks and routine verifications that dominate each new build.
Automating these tasks frees testers to focus on strategic analysis, creative exploration, and higher‑order problem solving. Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment, noting that many current tests are monotonous and could be better performed by an algorithm, allowing humans to concentrate on assessing gameplay and conducting inventive test scenarios. Both leaders stress that AI will never replace QA professionals; instead, it serves as an accelerator that pushes people toward more valuable work.
McBride describes AI as a “sliding scale” where the human element remains indispensable. He explains that the tool reduces human error and fatigue, resulting in higher‑quality output and fewer oversights. When asked about the types of games the solution currently supports, Side’s chief operating officer Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, graphically intensive 3D open‑world or fighting games remain a longer‑term goal.
Deslandes emphasizes that early adopters should be willing to experiment and iterate, as the platform is still evolving. In summary, the Side‑Modl.ai partnership aims to blend robust human QA expertise with AI‑driven automation, creating a hybrid workflow that improves efficiency, reduces monotony, and opens new career pathways for testers. While the system presently targets less complex games, the collaboration sets the stage for broader applicability as the technology matures, offering the gaming industry a promising glimpse into the future of intelligent quality assurance.