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 entrant to the gaming sector, was founded in 2018 and secured $1.7 million in seed capital two years later. While Side brings decades of experience in delivering testing and support services, 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. He adds that the gaming industry has long sought a "holy grail" – an AI tool that genuinely augments QA rather than merely automating repetitive steps. "No one’s really found a great solution for that.
We think we have." Earlier AI‑driven QA attempts, including Modl.ai’s own previous offering, required developers to ship full builds or even source code to the testing vendor. Those solutions relied on integration plugins and software development kits, creating friction and scalability problems. "It became difficult over time," recalls Modl.ai founder Christoffer Holmgard.
The new approach eliminates the need for internal access. Instead, developers provide the same product they would hand to a human tester – a compiled game ready for consumer use – and run it on a device that mirrors the target hardware. The system then watches the gameplay, simulates player inputs, records every on‑screen event, and feeds the resulting data set to a second AI model that scans for anomalies, flags potential bugs, and escalates findings to a human reviewer.
Holmgard describes the first phase as building "game knowledge." Developers upload assets, design documents, screenshots, or any other relevant material, and converse with the AI in plain language. "All of this is in natural language," he says.
"We just chat with the system about our game, but we’re also adding 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 scripted test cases to novel scenarios that exploit the AI’s strengths. Holmgard stresses that human expertise remains 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 platform generates comprehensive bug reports. These include textual descriptions, short video clips that serve as evidence, and full log files.
The output is handed to a seasoned QA specialist who reviews the findings and decides the appropriate next steps for the production pipeline. Holmgard notes a particular challenge for AI: distinguishing between an intentional feature and an unintended defect. "Feedback from QA testers helps the AI understand the nuances of game design," he explains.
Over time, as testers label reports as bugs, non‑bugs, or edge‑case features, the system fine‑tunes its judgment. Side envisions a broader impact on the workforce. By equipping QA staff with AI tools, the company hopes to elevate them from routine testing to roles akin to AI engineers.
"One of the cool parts about programs like this is that we get to move our people up the value chain," says McBride. Instead of a linear career path of tester → senior tester → lead tester, employees can transition into AI‑focused positions, earning higher salaries and gaining new skill sets.
This shift could also revive QA jobs in regions where low‑cost labor has previously driven outsourcing away, because higher‑value AI engineering work is less price‑sensitive. McBride further argues that AI can make QA careers more fulfilling.
Repetitive smoke tests and routine verifications often bog down testers, limiting their ability to think strategically. By automating those monotonous tasks, testers can focus on analysis, creative exploration, and higher‑order problem solving.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "There are some 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 are careful to stress that AI will not replace human QA workers. The price point for manual testing in certain low‑cost regions remains so low that a complete AI takeover is economically unrealistic.
Instead, the technology serves as a force multiplier, allowing existing staff to extend their careers and take on more sophisticated responsibilities. "There’s always going to be an opportunity for people to do a lot of the testing work here because the price point of testing right now is so low in certain countries that it’s very hard to augment it completely away," McBride notes. "But by giving our people tool sets like this, I’m allowing them to further their career. I’m allowing them to have a longer career outside.
And by moving them up the value chain, I get to keep them for longer." Side’s philosophy treats AI as a "sliding scale" where human oversight is never fully removed. "There will always be a human in the loop – that’s sort of our ethos," McBride affirms. The AI reduces human error, speeds up detection, and lifts overall quality, but the final judgment still rests with experienced QA professionals.
When asked about the types of games the solution can currently handle, COO Oliver Deslandes is candid: the technology is best suited for simpler titles such as puzzles, card games, or other experiences with repeatable mechanics. Fast‑paced 3D open‑world or fighting games remain a longer‑term goal. "We have to focus on titles that are more appropriate.
What we’re aiming for at this stage is really clients that are willing to come on the journey with us because it could very much depend on what they want to do with this," Deslandes explains. He adds that early adopters will likely be those seeking immediate cost savings and willing to pilot the system. In summary, the Side‑Modl.ai partnership aims to create a hybrid QA workflow where AI handles the heavy lifting of data collection, bug detection, and initial triage, while seasoned human testers provide the nuanced judgment and strategic insight that machines cannot yet replicate. The six‑month MOU will serve as a proving ground for this collaborative model, with the ultimate goal of reshaping QA careers, improving game quality, and demonstrating that AI, when paired with human expertise, can deliver a more efficient and satisfying testing process.