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 sets the stage for a joint effort to build an AI‑powered quality‑assurance (QA) platform that blends the strengths of each partner. 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 and support services to a broad range of clients, while Modl.ai contributes cutting‑edge machine‑learning expertise. "The MOU effectively brings the best of what Side has and the best of what Modl.ai has together in a unique solution," explains Martin McBride, Side’s chief commercial officer.
"One of the crown‑jewel, holy‑grail experiences for the games industry right now has been using AI technology to better augment QA services. No one's really found a great solution for that.
We think we have." Earlier AI‑driven QA tools, including Modl.ai’s first‑generation offering, required developers to ship entire builds—or even source code—over to the testing firm. That approach relied on integration plugins and SDKs, creating friction and scalability problems.
"It became difficult over time," admits Modl.ai founder Christoffer Holmgard. 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 eventually to a consumer. The AI runs the game on a device that mirrors the target platform, watches the screen, simulates key inputs, records every observable event, and then passes the resulting data set to a second AI layer that flags anomalies, categorises them as potential bugs, and escalates them for human review. The first phase of the workflow is what Modl.ai calls "building game knowledge." Developers upload assets, design documents, screenshots, or even short video clips, and then converse with the AI in plain language. "All of this is in natural language," Holmgard notes.
"We just chat with the system about our game, but we're also adding in images, or we might upload our game design documents and so on." Once the AI has a contextual understanding, the development team defines the test scope. This can range from classic regression cases to novel scenarios that are particularly suited to AI‑driven exploration. Holmgard stresses that human expertise is still essential at this stage: "You need some human expertise here to adapt the prompting and the language to the AI agent to make it do the right things." During a test run—referred to by Modl.ai as a "session"—the system records textual logs, video snippets (the so‑called "evidence"), and comprehensive telemetry.
These artefacts are bundled into a bug report and handed to a senior QA specialist for validation. "We get all the texts, and we get some video snippets that we call evidence along with all the log data and everything," Holmgard explains.
The human reviewer decides whether each flagged issue is a genuine defect, a intended feature, or a false positive, and provides feedback that the AI ingests to improve its future judgments. Distinguishing bugs from features remains a nuanced challenge. Over time, the AI learns from the accumulated feedback loop, refining its perception of what constitutes a problem in the context of a particular game’s design philosophy. "Some of it comes from the prompting.
Some of it comes from that game knowledge that you've built up around the game ahead of time," Holmgard says. "And then a good part of it also comes from feedback over time from the human testers." Side envisions this collaboration reshaping the career trajectory of its QA workforce. Rather than remaining in repetitive testing roles, staff can evolve into "AI engineers" who design prompts, interpret AI outputs, and fine‑tune models. McBride highlights the strategic benefit: "One of the cool parts about programs like this is that we get to move our people up the value chain.
So instead of just doing testing and having a career at Side being a tester and moving up to being a senior tester, they can become an AI engineer. It allows our people to get paid more, opens new career paths, and potentially brings jobs back to regions where they have historically disappeared because of low‑cost labour pressures." The automation of monotonous, repetitive checks also promises a more satisfying work experience.
"This tool reduces the burden of repetitive tests," McBride adds. "Smoke tests, verifications for every new build—those tasks bog down workflow and prevent strategic thinking. By automating them, testers can focus on analysis, creative problem‑solving, and higher‑level quality decisions." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "There are very tedious tests that testers are performing right now.
You don't really need a human doing the same flow over and over again. That person could be doing something much more valuable, like assessing the gameplay itself or doing more creative kinds of testing. That, I think, is a value proposition of using AI." Both leaders stress that AI will never fully replace human QA professionals. The technology is intended to augment, not supplant, the human element.
McBride explains that price pressures in low‑cost regions make it impossible to eliminate human testing entirely, but providing sophisticated AI tools enables staff to extend their careers and increase their earning potential. "There's always going to be an opportunity for people to do a lot of the testing work because the price point of testing right now is so low in certain countries that it's very hard to augment it completely away," he says. "By giving our people tool sets like this, I'm allowing them to further their career and keep them longer." Side’s philosophy on AI is described as a "sliding scale" where a human is always present in the loop.
"That's sort of our ethos," McBride affirms. "The tool lets us take the best of what AI can offer, reduce the burden, and also reduce mistakes. A lot of this work is tedious and prone to human error.
You take that out of the equation, and everything ratchets up a notch and becomes better." When asked about the types of games the solution currently supports, COO Oliver Deslandes notes that the system is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics—rather than fast‑paced 3D open‑world or fighting games. "We have to focus on titles that are more appropriate at this stage," he says.
"Clients who are willing to pilot the technology with us will see where it can go. It's a journey, and the early adopters will help shape its evolution." In summary, the Side‑Modl.ai partnership aims to blend AI efficiency with human insight, creating a QA ecosystem that speeds up bug detection, lowers repetitive workload, and opens new professional pathways for testers while maintaining a steadfast commitment to human oversight.