Side, a veteran 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 video games. The agreement binds the two companies to collaborate on a system that blends Side’s extensive QA expertise with Modl.ai’s cutting‑edge machine‑learning technology, aiming to create a more efficient, less labor‑intensive testing pipeline. Modl.ai, founded in 2018, is a relatively new player in the gaming sector but has already secured $1.7 million in seed financing.
Its chief commercial officer at Side, Martin McBride, describes the partnership as a marriage of the best assets each firm possesses. “One of the holy‑grail experiences the games industry is chasing is a way to augment QA services with AI in a reliable, scalable fashion. No one has cracked that problem yet, and we believe we have a viable solution,” he says. Earlier AI‑enabled QA tools, including Modl.ai’s own previous offerings, required developers to hand over entire builds or even source code to the testing provider.
Those integrations relied on plugins and SDKs that created friction and, over time, became cumbersome, according to Modl.ai founder Christoffer Holmgard. The new approach eliminates the need for any internal code access. Instead, developers supply the product exactly as they would to a human tester or eventual consumer—installed on a comparable device.
The AI then runs the game, monitors the screen, simulates inputs, records every observable event, and passes the resulting data to a second AI layer that flags anomalies, generates bug reports, and escalates items that merit human review. The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets—artwork, design documents, level files—and converse with the AI in natural language, teaching it the context and mechanics of the title. "We just chat with the system about our game, adding images or design docs as needed," Holmgard explains.
Once the AI has a baseline understanding, the development team defines the testing objectives, ranging from conventional test cases to novel scenarios that play to the strengths of the AI model. Human expertise remains crucial at this stage. "You need skilled people to craft prompts and guide the AI so it performs the right actions," Holmgard notes.
After the AI executes the defined sessions—referred to as "runs"—it compiles textual bug descriptions, short video snippets as evidence, and comprehensive log data. These artifacts are handed to a seasoned QA specialist who validates the findings, determines severity, and decides on the appropriate remediation steps. One persistent challenge for any automated system is distinguishing a genuine defect from an intentional feature.
Holmgard says that continuous feedback from human testers helps the AI refine its judgment. "Prompting, pre‑built game knowledge, and iterative feedback all contribute to the model learning what constitutes a bug versus a design choice," he says. Over time, the system becomes attuned to the nuances of each project.
Side envisions a broader transformation for its workforce. By upskilling QA staff into AI engineers, the company hopes to move talent up the value chain, offering higher‑pay roles and new career trajectories. "Instead of a linear path from junior tester to senior tester, our people can become AI engineers, which opens up better compensation and the chance to bring jobs back to regions where low‑cost testing has been off‑shored," McBride explains.
He adds that AI will relieve testers of repetitive, mundane tasks, freeing them to focus on strategic analysis and creative problem‑solving, ultimately leading to more fulfilling careers. Modl.ai’s chief product officer Benedikte Mikkelsen echoes this sentiment: many current tests are monotonous and can be automated, allowing humans to concentrate on higher‑order evaluation such as gameplay feel and innovative testing techniques.
This, she argues, is the core value proposition of AI‑augmented QA. Both leaders stress that AI is not a replacement for human QA workers but a tool that elevates their role.
McBride points out that testing costs remain low in certain regions, making a full AI takeover impractical. However, by providing sophisticated tooling, Side can retain talent longer and give them opportunities for professional growth. The partnership’s philosophy treats AI as a "sliding scale"—a spectrum where the proportion of automation can be adjusted, but a human will always remain in the loop. "That’s our ethos," McBride says.
"The tool lets us capture the best of AI while reducing tedious work and human error, raising overall quality. When asked about the types of games the solution currently supports, Side’s chief operating officer Oliver Deslandes admits 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. "We’re focusing on clients willing to embark on this journey with us, even if they have modest expectations at first," Deslandes explains.
He adds that pilot projects will reveal how the technology evolves and scales. In summary, the Side‑Modl.ai MOU represents a strategic effort to blend human expertise with artificial intelligence in game quality assurance. By automating routine testing, enriching AI models with developer‑provided knowledge, and keeping seasoned QA professionals in supervisory roles, the partnership aims to deliver faster, more accurate bug detection while creating new, higher‑value career paths for the testing workforce.