Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding with Danish artificial‑intelligence specialist Modl.ai to co‑develop an AI‑powered quality‑assurance platform for video games. The agreement binds the two companies to collaborate on a system that blends Side’s extensive QA experience with Modl.ai’s cutting‑edge machine‑learning technology, aiming to create a more efficient and scalable testing process while keeping a human reviewer in the loop. Modl.ai, founded in 2018, broke into the games market after securing $1.7 million in seed financing in 2020.
Its early AI‑based QA tools required developers to ship full builds, source code, or even integrate a proprietary SDK into their projects. This "integration‑heavy" approach generated friction: developers balked at handing over internal assets, and the process grew cumbersome over time, according to founder Christoffer Holmgard.
The new solution sidesteps those hurdles by treating the game exactly as a QA tester or end‑consumer would receive it—no source code, no deep hooks, just the compiled product running on a target device. The workflow begins with what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, or any other relevant material, and then converse with the AI in natural language. The system absorbs this information, forming a contextual model of the game’s mechanics, narrative, and visual style. Once the AI has a solid grasp, the development team defines the test scope—ranging from traditional regression cases to novel scenarios that leverage the AI’s pattern‑recognition capabilities.
Holmgard stresses that crafting effective prompts still needs human insight; testers must translate their testing goals into language the AI can act upon. During a test run, the AI launches the game on a device, monitors the screen, simulates inputs, and records a comprehensive data set of every event that occurs. A second AI layer then parses this data, flagging anomalies that could indicate bugs. The system produces a bug report that includes textual descriptions, short video clips as evidence, and full log files.
These reports are handed to seasoned QA specialists, who verify the findings, classify each issue as a genuine defect or an intended feature, and decide on the appropriate remediation steps. One of the enduring challenges in automated testing is distinguishing a bug from a deliberate game mechanic. Holmgard explains that the AI improves its judgment over time through two feedback loops: prompt engineering from testers and direct annotation of bug reports.
When a human marks a finding as "not a bug" and provides a rationale, the AI incorporates that nuance, gradually refining its internal model of what constitutes acceptable behavior within that particular game. Side sees this technology as a catalyst for career evolution within its QA workforce. Chief Commercial Officer Martin McBride envisions testers transitioning into "AI engineers"—roles that involve configuring prompts, interpreting AI‑generated insights, and guiding the system toward more sophisticated testing strategies. By moving staff up the value chain, Side hopes to raise salaries, open new professional pathways, and even repatriate jobs to regions where low‑cost testing has previously migrated.
McBride also argues that AI can make QA careers more fulfilling. Repetitive smoke tests and routine verification steps consume a large portion of a tester’s day, limiting opportunities for strategic thinking.
Automating those mundane tasks frees human analysts to focus on higher‑order activities such as gameplay analysis, risk assessment, and creative test design. This shift not only enhances job satisfaction but also reduces human error, as the AI consistently executes the same steps without fatigue.
Benedikte Mikkelsen, Modl.ai’s Chief Product Officer, echoes this sentiment, noting that many current test cases are overly tedious for humans to repeat. "If you can automate a monotonous flow, the tester can devote their expertise to evaluating the fun factor, exploring edge cases, or inventing novel testing methodologies," she says. This value proposition aligns with Side’s philosophy that AI should augment—not replace—human talent. Both companies acknowledge that the technology is not yet ready for every genre.
Oliver Deslandes, Side’s Chief Operating Officer, points out that the system currently excels with simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, 3‑D open‑world or fighting games present additional complexities that the AI will need more training to handle. Consequently, early pilots will target developers willing to experiment and invest in a collaborative learning process. In summary, the Side‑Modl.ai partnership aims to deliver an AI‑enhanced QA platform that respects the constraints of developers, leverages natural‑language interaction, and maintains a human‑in‑the‑loop oversight model.
By automating repetitive testing chores, the solution aspires to elevate QA professionals into more analytical, higher‑value roles, potentially reshaping the global distribution of testing talent. While the technology is still maturing and best suited for less complex games at this stage, the partners view the collaboration as a stepping stone toward broader applicability and a more sustainable, AI‑augmented future for game quality assurance.