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‑driven quality‑assurance (QA) platform that blends Side’s extensive testing expertise with Modl.ai’s cutting‑edge machine‑learning technology. 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.
Its founders envision AI as a catalyst for a more efficient, less repetitive QA process, but they also recognise that earlier attempts at AI‑enabled testing have stumbled over practical obstacles. In prior versions, developers were required to hand over complete game builds—or even source code—so that the AI could analyse the product.
That approach, which relied on integration‑heavy plugins and SDKs, proved cumbersome and created friction that limited adoption, according to co‑founder Christoffer Holmgard. Holmgard explains that the new solution sidesteps those hurdles entirely. "We treat the product exactly as you would hand it to a human tester or eventually ship to a consumer," he says.
"The game runs on a device that mirrors the end‑user environment, our system watches the screen, simulates key inputs, records every observable event, and then hands the resulting data set to a second AI layer that flags anomalies, surfaces 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, and other relevant material, then converse with the AI in natural language to teach it the fundamentals of the title. This conversational interface allows the system to absorb visual cues, narrative context, and mechanical details without needing direct access to the underlying code. Once the AI has a baseline understanding, the development team defines the testing scope. Test cases can range from traditional, scripted scenarios to novel, AI‑friendly challenges that exploit the system’s pattern‑recognition strengths.
Holmgard stresses that human expertise remains essential at this stage: "You need to craft prompts and guide the AI so it performs the right actions. It’s a collaborative choreography between people and machine." During a test run—referred to by Modl.ai as a "session"—the platform records textual logs, short video snippets (the so‑called "evidence"), and comprehensive telemetry. These artefacts are then passed to a human QA specialist, who validates the findings, decides which issues merit further action, and provides feedback that helps the AI refine its judgement over time. Distinguishing a genuine bug from an intentional feature can be especially tricky for an algorithm, so the iterative loop of human correction and machine learning is crucial.
Side sees this feedback loop as an opportunity to reshape the career trajectory of its testing workforce. Chief Commercial Officer Martin McBride explains that, rather than remaining in a purely execution‑focused role, QA staff can evolve into "AI engineers" who design prompts, interpret AI‑generated insights, and oversee the integration of automated testing into broader production pipelines. This upskilling, he argues, not only raises salaries and job satisfaction but also creates the possibility of bringing QA jobs back to regions where low‑cost labour has historically driven outsourcing. "AI takes care of the repetitive smoke tests and routine verifications that bog down testers," McBride notes.
"When those chores are automated, our people can focus on strategic analysis, creative problem‑solving, and higher‑level quality decisions, making their careers more fulfilling." Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: "There are many tedious test flows that a human can repeat ad infinitum without adding value. By automating those, testers can shift their attention to assessing gameplay feel, exploring edge‑case scenarios, and performing more inventive testing activities. That’s the core value proposition of AI in QA." Both leaders are clear that AI will not replace human testers but will augment them. McBride describes AI as a "sliding scale" where the human element remains in the loop at every stage.
"Our ethos is that there will always be a person overseeing the process," he says. "The tool reduces manual workload and human error, raising overall quality while preserving the essential judgment that only experienced professionals can provide." When asked about the types of games the system currently supports, COO Oliver Deslandes admits the technology 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 are focusing on projects where the gameplay loops are predictable and the visual feedback is easy for the AI to interpret," he explains. "Clients willing to pilot this technology with us now will help shape its evolution toward more complex genres." In summary, the Side‑Modl.ai partnership aims to create a hybrid QA model that leverages AI for speed and consistency while retaining human expertise for nuance and strategic insight.
By treating AI as an assistive layer rather than a replacement, both companies hope to deliver higher‑quality game releases, open new career pathways for QA professionals, and gradually expand the technology’s applicability across a broader spectrum of game types as the underlying models mature.