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. The agreement binds the two companies to co‑develop an AI‑enhanced quality‑assurance (QA) platform that aims to reshape how video‑game testing is performed.
Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed financing two years later. Side brings decades of experience delivering testing, localisation and support services to publishers worldwide. Together, they hope to combine their respective strengths into a solution that could become a “holy grail” for the industry, according to Side’s chief commercial officer Martin McBride.
“Historically, AI‑based QA has struggled to deliver a seamless, low‑friction workflow,” McBride explains. “Developers were forced to ship entire builds or even share source code so that the AI could run its analyses. That approach created security concerns, slowed down pipelines and rarely produced the level of insight that human testers could provide.
Our partnership with Modl.ai is designed to change that.” The earlier generation of Modl.ai’s technology relied on an integration model that required a plug‑in, an SDK and direct access to a game’s codebase. As founder Christoffer Holmgard admits, that method “became difficult over time” because of the overhead it imposed on developers. The new system eliminates the need for any internal access.
Instead, the game is delivered to the AI platform exactly as it would be handed to a human tester or, ultimately, to a consumer. "You place the build on a device that mirrors the target hardware, and our solution runs the game by watching the screen, simulating inputs and capturing every interaction," Holmgard says. "The AI then creates a comprehensive dataset of what happened, passes it to a second‑stage model that flags potential bugs, and finally escalates those findings to a human reviewer for validation." The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots and other reference material, then converse with the AI in natural language to teach it the specifics of their title.
This conversational approach lets the system absorb the game’s mechanics, narrative context and visual style without ever seeing the underlying code. Once the AI has a baseline understanding, developers specify the testing objectives.
These can range from conventional test cases—such as verifying UI elements or level loading times—to more exploratory scenarios that are tailored to the strengths of machine‑learning models. Holmgard stresses that human expertise remains essential at this stage: “You need to craft prompts and guide the AI so it knows exactly what to look for. That translation from human intent to machine instruction is where seasoned QA professionals add the most value.” During a test run, the platform records textual logs, short video clips (referred to as “evidence”), and other telemetry. The AI then compiles this information into bug reports, which are handed to a human QA specialist for review.
The specialist decides whether each finding is a genuine defect, an intended feature, or a false positive, and provides feedback that the system uses to refine its future predictions. One of the most challenging aspects of automated testing is distinguishing a bug from a deliberate gameplay mechanic. Holmgard notes that iterative feedback loops—both from the prompting stage and from human reviewers—gradually teach the AI the subtle nuances of game design.
Over time, the model becomes better at recognizing when a quirky behavior is an intentional Easter egg versus an actual error. Side sees this technology as a catalyst for career evolution within its workforce. McBride envisions QA staff transitioning into “AI engineers,” a shift that would move them up the value chain and open higher‑paying, more strategic roles. By automating repetitive smoke tests and regression checks, the AI frees testers to focus on higher‑order analysis, creative exploration and gameplay assessment.
“This tool reduces the burden of monotonous testing,” McBride explains. “When you automate those baseline checks, testers can spend their time thinking strategically, interpreting data and contributing to design decisions. That makes the job more fulfilling and creates new pathways for professional growth.” Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes the sentiment: “There are many tedious, repetitive flows that a human can execute indefinitely. By handing those to AI, we let people apply their expertise to more valuable tasks, such as evaluating game balance or inventing novel test scenarios.” Both leaders are careful to stress that AI will not replace human QA workers.
Instead, it serves as an augmentation tool that expands capabilities while preserving the critical human judgment element. McBride points out that testing costs remain low in certain regions, making a total automation approach economically unfeasible. Providing AI‑enhanced toolkits allows Side to retain talent, extend careers and keep work in markets that have previously seen outsourcing drift away.
The partnership also adopts a philosophical stance on automation. Side describes AI as a “sliding scale” where human involvement is always present: “Our ethos is that there will always be a human in the loop.
AI helps us cut errors and speed up processes, but the final decision rests with experienced professionals.” When asked about the types of games the system can handle today, chief operating officer Oliver Deslandes notes that the current focus is on less complex titles—puzzle games, card games and other experiences with repeatable mechanics. Fast‑paced 3D open‑world or fighting games remain a longer‑term goal, as they demand more sophisticated perception and decision‑making capabilities.
Deslandes adds, “We’re looking for partners willing to embark on this journey with us, even if they start with pilot projects. The technology will evolve, and as we gather more data, we’ll be able to broaden the scope of games it can support.” In summary, the Side‑Modl.ai MOU represents a concerted effort to blend AI efficiency with human insight in video‑game quality assurance. By offering a workflow that requires no code‑level access, leveraging natural‑language interaction, and preserving a human review stage, the collaboration aims to deliver faster, more accurate bug detection while opening new career pathways for QA professionals. The six‑month trial will serve as a proving ground for this vision, with the potential to reshape testing practices across the industry.