Side, a veteran 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 (QA) platform for the gaming industry. 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 solution that augments, rather than replaces, human testers.
### Background and Motivation Side has long been a mainstay in the outsourced services market, delivering testing, localization, and support services to game developers worldwide. Modl.ai, founded in 2018, is a newer entrant that secured $1.7 million in seed capital in 2020 and has focused on applying AI to the challenges of game testing. While previous AI‑enabled QA tools required developers to hand over complete builds or even source code, often via cumbersome plugins and SDKs, the partners argue that those approaches created too much friction and limited adoption. According to Modl.ai’s founder Christoffer Holmgard, the new system eliminates the need for deep integration.
“We take the product exactly as you would hand it to a QA tester or eventually to the consumer,” he explains. “The game runs on a device that mirrors the end‑user environment, our AI watches the screen, simulates key inputs, records every observable event, and then passes that data to a second AI layer that flags anomalies, generates bug reports, and escalates anything that requires human judgment.” ### Building Game Knowledge The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, and even converse with the AI in natural language to teach the system about the game’s mechanics, narrative, and visual style. This conversational approach lets the AI construct a semantic model of the title without ever seeing the underlying code.
Holmgard notes that the process is deliberately language‑centric: “We just chat with the system about our game, we add images, we feed design docs—everything is expressed in plain English.” ### Defining Test Scenarios Once the AI has a baseline understanding, developers outline the testing objectives. These can range from traditional test cases—such as verifying UI navigation or ensuring that a save‑load cycle works—to more exploratory scenarios that are specifically crafted to leverage the AI’s pattern‑recognition capabilities.
Holmgard stresses that human expertise is still crucial at this stage: “You need to shape the prompts and the language so the AI agent does the right thing. It’s a collaborative prompt‑engineering exercise.” ### Automated Bug Detection and Human Review During a “run” (Modl.ai’s term for a testing session), the AI records screen video, logs input events, and captures system telemetry.
After the session, it produces a comprehensive bug report that includes textual descriptions, short video snippets as evidence, and raw log data. These reports are then handed to a human QA specialist for validation.
The specialist decides whether each finding is a genuine defect, a design feature, or a false positive, and provides feedback that the AI uses to refine its future judgments. One of the persistent challenges in automated testing is distinguishing a bug from an intentional feature.
Holmgard explains that the system learns from two sources: the initial game knowledge built through natural‑language interaction, and ongoing feedback from human testers. Over time, this iterative loop “attunes” the AI, improving its ability to make nuanced decisions about what constitutes a defect. ### Upskilling QA Personnel Side sees the partnership as a pathway to elevate its workforce. Rather than remaining in repetitive, low‑margin testing roles, staff can transition into “AI engineers” who design prompts, curate training data, and interpret AI‑generated insights.
Chief Commercial Officer Martin McBride argues that this shift moves employees up the value chain, enabling higher wages, new career trajectories, and the possibility of bringing jobs back to regions where they had previously migrated due to cost pressures. McBride also highlights the impact on job satisfaction: “By automating the monotonous smoke tests and regression checks, our testers can focus on strategic analysis, creative exploration, and higher‑level problem solving. That makes their careers more fulfilling and helps them grow professionally.” ### The Human‑in‑the‑Loop Ethos Both companies stress that AI is an assistant, not a replacement.
McBride describes AI as a “sliding scale” where the degree of automation can be adjusted, but a human will always remain in the loop. This philosophy mirrors Side’s broader ethos that technology should amplify human talent while reducing error‑prone, tedious work. “People make mistakes when they’re doing the same repetitive task over and over,” he says.
“Removing that from the equation lets the overall process ratchet up in quality.” ### Current Scope and Future Outlook Side’s Chief Operating Officer Oliver Deslandes notes that the solution is presently best suited to simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games. “We’re focusing on titles where the AI can reliably interpret screen data and where the gameplay loop is not too rapid,” he explains. The partners view the current pilots as a stepping stone; as the technology matures, they anticipate expanding to more complex genres.
### Conclusion The Side‑Modl.ai collaboration represents a pragmatic blend of seasoned QA expertise and modern AI capabilities. By sidestepping the need for deep code integration, leveraging natural‑language interaction to build game knowledge, and maintaining a robust human‑in‑the‑loop review process, the partnership aims to deliver a more efficient, accurate, and satisfying QA experience. In the longer term, the initiative could reshape the global QA labor market, creating higher‑skill roles and potentially repatriating jobs to regions that have lost them to cost‑driven outsourcing.
The journey is just beginning, but early pilots suggest that AI‑augmented testing could become a cornerstone of game development pipelines in the years to come.