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 partnership aims to develop a hybrid, AI‑powered quality‑assurance (QA) platform that blends the strengths of each company into a single, market‑ready solution. Modl.ai, a relatively new player in the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later.
While Side brings deep experience in service delivery and a global QA workforce, Modl.ai contributes cutting‑edge machine‑learning algorithms and a vision for how AI can augment, rather than replace, human testers. Martin McBride, Side’s chief commercial officer, summed up the collaboration: “The MOU essentially fuses the best of Side’s operational expertise with Modl.ai’s AI technology into a unique offering.
One of the industry’s holy‑grail ambitions right now is to use AI to enhance QA services, and we believe we have found a workable path.” Historically, AI‑driven QA tools required developers to ship entire builds—or even source code—to the testing vendor. Modl.ai’s earlier approach relied on an integration plug‑in and SDK, forcing studios to expose internal assets and creating considerable friction. Founder Christoffer Holmgard admits that this model “became difficult over time.” The new system eliminates the need for any internal access.
Instead, developers provide the product exactly as they would hand it to a human tester or eventual consumer. The game runs on a device that mirrors the target platform, while the AI watches the screen, mimics player inputs, records every visual and data point, and then hands the resulting dataset to a second AI layer that flags anomalies, generates bug reports, and escalates items that require human judgment.
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 plain English (or any natural language). The AI ingests these inputs, creating a semantic model of the game’s mechanics, assets, and intended behavior. Holmgard explains, “We just chat with the system about our game, but we also feed it images, design docs, and other artifacts, so it builds a contextual understanding.” Once the AI has a baseline understanding, developers specify the testing scope.
This can range from traditional test cases—such as level‑load verification or UI consistency checks—to novel scenarios that are deliberately crafted to exploit the AI’s pattern‑recognition capabilities. Holmgard notes that human expertise remains essential at this stage: “You need to shape the prompts and language so the AI knows what to do.” During a test run, the system records logs, video snippets (referred to as “evidence”), and textual descriptions of any irregularities. These artifacts are compiled into a comprehensive bug report and passed to a human QA specialist for review.
The specialist decides whether the issue is a genuine defect, an intended feature, or a false positive, and provides feedback that the AI uses to refine its future predictions. Distinguishing bugs from intentional features is a known challenge for automated testing.
Holmgard stresses that continuous feedback loops—both from the prompting stage and from human reviewers—help the AI learn the subtle design nuances that separate a purposeful mechanic from an unintended glitch. Over time, the model becomes increasingly attuned to the studio’s specific design philosophy. Side envisions a broader shift in the QA workforce.
By equipping testers with AI tools, the company hopes to elevate many employees from routine test execution to roles akin to AI engineers. McBride says this “moves our people up the value chain,” enabling them to command higher salaries, pursue new career trajectories, and potentially bring jobs back to regions where low‑cost testing had previously migrated away. The automation of repetitive smoke tests and regression checks 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: “There are many tedious, repetitive tests that a human doesn’t need to repeat endlessly. By automating those, testers can concentrate on assessing gameplay feel, exploring edge‑case scenarios, and delivering more insightful feedback.” Both leaders are careful to frame AI as a complement, not a replacement.
McBride emphasizes that the price point of manual testing in certain low‑cost regions remains so low that a full AI takeover is unrealistic. Instead, the AI serves as a force multiplier, allowing existing staff to extend their careers, take on more complex responsibilities, and stay engaged longer. The partnership’s guiding philosophy is encapsulated in McBride’s description of AI as a “sliding scale.” He asserts, “There will always be a human in the loop—that’s our ethos.” By removing monotonous tasks and reducing human error, the AI tool raises overall quality and consistency, while preserving the critical judgment that only experienced QA professionals can provide.
When asked about the types of games the solution can currently handle, Chief Operating Officer Oliver Deslandes admits the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable, slower mechanics. Fast‑paced, 3D open‑world or fighting games present greater technical hurdles and are slated for later development phases. Deslandes adds that early adopters will likely be studios willing to experiment and co‑develop the system, accepting the trade‑off between immediate cost savings and the longer‑term potential of AI‑enhanced QA. In summary, the Side‑Modl.ai MOU represents a pragmatic step toward integrating artificial intelligence into game testing while retaining human expertise at its core.
By automating low‑value tasks, providing a structured knowledge‑building pipeline, and fostering a feedback‑driven learning loop, the collaboration aims to deliver faster, more reliable bug detection and open new career pathways for QA professionals. The journey is still early, but participants who engage in pilot projects now will help shape the next generation of AI‑assisted quality assurance for the gaming industry.