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 agreement sets the stage for a joint venture that will blend Side’s deep experience in quality assurance (QA) with Modl.ai’s cutting‑edge AI technology to create a next‑generation, AI‑enabled QA platform for the gaming industry.
The partnership is significant for several reasons. First, it brings together two complementary skill sets: Side’s long‑standing network of QA professionals and Modl.ai’s innovative AI models that have been under development since the company’s 2018 inception and subsequent $1.7 million seed round. Second, the collaboration aims to solve a persistent pain point for developers—how to harness AI for testing without exposing proprietary code or creating cumbersome integration steps. Martin McBride, Side’s chief commercial officer, explains the vision: “The MOU essentially fuses the strongest assets of Side and Modl.ai into a single, differentiated solution.
The gaming sector has been searching for a reliable way to augment QA with AI, and we believe we have finally cracked it.” Historically, AI‑driven QA tools required developers to ship entire builds, often including source code, to a testing vendor. Modl.ai’s earlier offering relied on an integration‑heavy approach involving plugins and SDKs, a method that proved friction‑laden and difficult to scale, according to founder Christoffer Holmgard. The new system, however, eliminates the need for any internal access.
Holmgard describes it as follows: “We treat the product exactly as you would hand it to a human tester—or eventually to a consumer. The game runs on a device that mirrors the target platform, our AI watches the screen, simulates inputs, records every interaction, and then passes the resulting data to a second AI layer that flags potential bugs and escalates them for human review.” The workflow begins with what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots, and other relevant materials, then converse with the AI in plain English (or any natural language) to teach it the fundamentals of the game world.
This conversational interface allows the system to ingest both visual and textual information, creating a rich contextual model of the title under test. Once the AI has a baseline understanding, developers specify the testing objectives. These can range from traditional test cases—such as verifying UI elements or checking level loading times—to more exploratory scenarios that are uniquely suited to AI, like probing edge‑case player behaviours. Holmgard stresses that human expertise remains essential at this stage: “Crafting effective prompts and guiding the AI’s language is an art.
QA specialists need to translate their domain knowledge into instructions the AI can act upon.” During a test run, the AI records a comprehensive data set that includes log files, video snippets (referred to as “evidence”), and textual summaries of observed anomalies. This package is handed off to a human QA analyst for validation. The analyst reviews each report, decides whether an issue is a genuine bug, a design feature, or a false positive, and provides feedback that the system uses to refine its future predictions.
Over time, this feedback loop helps the AI differentiate subtle nuances of game design that might otherwise be misinterpreted. One of the most challenging aspects of automated testing is distinguishing a bug from an intentional feature.
Holmgard notes that continuous feedback from human testers is crucial: “The AI learns from the prompts we give it, from the game knowledge we built beforehand, and from the corrective input we receive after each session. This iterative training makes the system increasingly attuned to the developer’s intent.” Side envisions a broader strategic impact: QA professionals could evolve into AI engineers, shifting their career trajectories upward along the value chain. McBride explains, “Instead of a linear path from junior tester to senior tester, our staff can acquire AI‑engineering skills, command higher salaries, and open new career pathways. This also allows us to bring jobs back to regions where they were previously outsourced because the work becomes higher‑value and less price‑sensitive.” The introduction of AI also promises to make QA roles more rewarding.
By automating repetitive, low‑level tasks—such as smoke testing each new build—testers can focus on strategic analysis, creative problem‑solving, and deeper gameplay evaluation. McBride adds, “When the mundane chores are handled by the AI, our people can think critically, conduct root‑cause analysis, and contribute to design improvements, which leads to a more fulfilling career.” Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: “Many current tests are monotonous and involve running the same flow thousands of times. Automating those frees human testers to assess game feel, balance, and other creative dimensions that truly add value.” Both leaders are clear that AI will not replace QA staff; rather, it will augment them. McBride emphasizes that the price point for manual testing in low‑cost regions remains a factor, making a complete AI takeover impractical for now.
“Our goal is to equip our people with tools that enhance their capabilities, extend their careers, and keep them engaged,” he says. “There will always be a human in the loop—that’s our ethos.” When asked about the types of games the solution can handle today, Chief Operating Officer Oliver Deslandes notes that the technology is best suited for titles with relatively simple, repeatable mechanics—puzzle games, card games, and other low‑speed experiences.
“Fast‑paced 3D open‑world or fighting games are still a ways off,” he admits. “We’re focusing on clients who are willing to pilot the technology and accept that the journey will involve iterative improvements.” In summary, the Side‑Modl.ai partnership aims to create a hybrid QA ecosystem where AI handles high‑volume, repetitive testing while seasoned human experts provide strategic oversight, validation, and continuous training. By positioning AI as a sliding scale rather than a full replacement, the collaboration seeks to raise the overall quality of game releases, improve tester satisfaction, and potentially reshape the global distribution of QA talent.