Side, a seasoned services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Denmark‑based artificial‑intelligence specialist Modl.ai. The partnership is aimed at creating an AI‑enhanced quality‑assurance (QA) platform that blends Side’s deep testing experience with Modl.ai’s cutting‑edge machine‑learning technology. ### Background and Strategic Fit Side has been a mainstay in the outsourcing and testing arena for many years, offering a range of services to game developers and publishers.
Modl.ai, by contrast, is a relatively new player in the gaming sector. Founded in 2018, the startup secured $1.7 million in seed capital two years later and has since focused on building AI tools that can automate parts of the QA process.
Martin McBride, Side’s chief commercial officer, describes the MOU as a union of complementary strengths: “We are bringing together the best of Side’s testing pedigree and Modl.ai’s AI capabilities to deliver a solution that the games industry has been chasing for a long time.” ### How the New System Differs from Earlier AI‑Based QA Earlier attempts at AI‑driven QA, including Modl.ai’s own first‑generation offering, required developers to hand over full builds or even source code. Those solutions relied on integration plugins and SDKs, creating friction and limiting scalability.
According to Modl.ai founder Christoffer Holmgard, the new approach eliminates the need for any internal access. Instead, the product is supplied to the AI system exactly as a QA tester would receive it—installed on a device that mirrors the end‑user’s hardware. The AI then "plays" the game, watches the screen, simulates inputs, and records a comprehensive data set of every in‑game event.
That raw data is passed to a second AI layer that scans for anomalies, flags potential bugs, and escalates findings to a human reviewer. In Holmgard’s words, the system "takes the product as you would give it to a QA tester, as you would eventually give it to the consumer," then generates evidence such as video snippets, logs, and textual descriptions for a specialist to evaluate.
### Building "Game Knowledge" Through Natural‑Language Interaction The first stage of the workflow involves creating what Modl.ai calls "game knowledge." Developers upload assets—artwork, design documents, code snippets, or any relevant material—and converse with the AI using plain language. "All of this is in natural language," Holmgard explains. "We just chat with the system about our game, but we’re also adding images or design docs, and the AI builds an understanding of the title." Once the AI has a baseline comprehension, developers specify the testing objectives. These can range from classic regression test cases to novel scenarios that are deliberately crafted to exploit the AI’s strengths.
Holmgard stresses that human expertise remains essential at this juncture: "You need to adapt the prompting and language to guide the AI agent to do the right things." ### From AI‑Generated Findings to Human Decision‑Making When a test run—referred to by Modl.ai as a "session"—concludes, the system compiles a bug report that includes textual descriptions, video evidence, and full log data. This package is handed over to a seasoned QA specialist who reviews the findings, decides which issues merit further action, and provides feedback to the AI.
The feedback loop helps the model learn the subtle distinction between a genuine bug and an intended feature, a nuance that can be especially tricky in game design. Holmgard notes that the AI improves over time through three sources of input: the initial prompting, the pre‑built game knowledge, and continuous feedback from human testers.
"As testers label reports as ‘bug’, ‘not a bug’, or ‘not a bug for this reason’, the system becomes attuned to the developer’s design philosophy." ### Upskilling QA Staff into AI Engineers Side envisions a future where traditional QA roles evolve into AI‑engineering positions. McBride explains that this shift can lift staff up the value chain, offering higher salaries, new career trajectories, and the possibility of bringing jobs back to regions where they were previously outsourced for cost reasons.
"Instead of a linear path from junior tester to senior tester, our people can become AI engineers, which adds value for both the employee and the company," he says. The AI tool also promises to make QA work more fulfilling by automating repetitive, low‑skill tasks such as smoke tests and routine verifications. By removing these chores, testers can focus on strategic analysis, creative testing, and deeper gameplay evaluation. Benedikte Mikkelsen, Modl.ai’s chief product officer, adds, "There are very tedious tests that don’t need a human to repeat over and over.
Those humans can instead assess gameplay or perform more inventive testing, which is the real value proposition of AI." ### Human Oversight Remains Central Both sides stress that AI will never fully replace human QA professionals. The price of testing in certain low‑cost regions remains so low that a complete AI takeover is economically impractical. However, by providing advanced tooling, Side can retain talent longer, give employees a broader skill set, and maintain higher quality standards. McBride summarizes the philosophy: "There will always be an opportunity for people to do testing work because the price point is low in some countries, but with these tools we enable them to move up the value chain and have longer, more rewarding careers." ### Current Scope and Future Roadmap Oliver Deslandes, Side’s chief operating officer, clarifies that the present version of the AI solution is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics.
Fast‑paced genres such as fighting games or large open‑world adventures are still on the roadmap but require further development. "We’re focusing on titles that are appropriate for the technology today and working with clients who are willing to pilot the solution," Deslandes says. He acknowledges that the journey is ongoing: "If you’re at the table with us now and doing pilots, you’ll see where the tech goes." ### Conclusion The Side‑Modl.ai partnership represents a significant step toward integrating artificial intelligence into game quality assurance while preserving the indispensable role of human expertise.
By combining Side’s extensive testing infrastructure with Modl.ai’s AI‑driven analysis, the collaboration aims to reduce repetitive workload, improve bug detection accuracy, and create new career pathways for QA professionals. As the technology matures, it is expected to expand beyond simple games to more complex titles, potentially reshaping the global QA landscape while adhering to the shared ethos that a human will always remain in the loop.