Side, a well‑established 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 a joint AI‑driven quality‑assurance (QA) platform that blends Side’s deep testing expertise with Modl.ai’s cutting‑edge machine‑learning capabilities.
### Background of the partners Side has been a fixture in the outsourced services arena for many years, offering a broad suite of testing, localisation and support functions to game developers worldwide. Modl.ai, by contrast, is a newer player that broke into the gaming sector in 2018 and secured $1.7 million in seed funding two years later. While Modl.ai has already experimented with AI‑enabled QA tools, those early versions required developers to hand over full game builds—or even source code—so that the AI could analyse the product from the inside out.
This “integration‑based” approach proved cumbersome, creating friction for studios that were reluctant to expose proprietary assets. ### A new, less‑intrusive workflow According to Modl.ai founder Christoffer Holmgard, the upcoming system eliminates the need for any internal access.
"We take the product exactly as you would hand it to a human tester, or eventually to the consumer," he explains. The game is installed on a device that mirrors the target platform, and the AI watches the screen, simulates player inputs, records every visual and behavioural cue, and then hands the resulting data set to a second AI layer that flags potential defects. Human reviewers then decide which issues merit further action. The process begins with what Modl.ai calls “building game knowledge.” Developers upload assets, design documents, screenshots and other relevant material, and they converse with the AI in plain English (or any supported natural language).
This dialogue helps the system form a contextual model of the game’s mechanics, narrative and visual style. Once the AI has a sufficient understanding, the development team outlines the test scenarios they wish to run—ranging from traditional regression cases to novel, AI‑optimised challenges. Holmgard stresses that human expertise remains vital at this stage.
"You need to craft prompts and language that guide the AI to behave the way you expect," he says. The AI then executes the defined sessions, collecting logs, video snippets (referred to as “evidence”), and textual descriptions of any anomalies it observes. These artifacts are passed to a seasoned QA specialist who validates the findings, categorises them as bugs or intended features, and provides feedback that the system uses to refine its future assessments. ### The human‑in‑the‑loop philosophy Both companies underline that AI is not a replacement for human testers but a tool that lifts them up the value chain.
Side’s chief commercial officer Martin McBride describes the partnership as “bringing the best of what Side has and the best of what Modl.ai has together in a unique solution.” He envisions QA staff evolving into AI engineers, gaining higher‑paying, more strategic roles that focus on analysis, test design and creative problem‑solving rather than repetitive, low‑skill chores. McBride explains that many current QA tasks—smoke tests, basic verification steps, and repetitive gameplay loops—are tedious and error‑prone. By automating these with AI, testers can devote their attention to higher‑order activities such as gameplay evaluation, performance optimisation and nuanced bug triage. This shift not only makes careers at Side more fulfilling but also opens the possibility of reshoring jobs to regions where low‑cost testing has previously been outsourced.
"When you move people up the value chain, you can bring them back to countries where they historically left," McBride notes. Modl.ai’s chief product officer Benedikte Mikkelsen echoes this sentiment, pointing out that the most monotonous tests—repeating the same flow over and over—are ideal candidates for automation. Freeing testers from such drudgery allows them to apply their expertise to more creative testing scenarios, ultimately delivering higher quality games. ### Limitations and future roadmap When asked about the types of games the system can currently handle, Side’s chief operating officer Oliver Deslandes is candid: the AI works best with simpler titles such as puzzles, card games and other experiences with repeatable mechanics.
Fast‑paced, graphically intensive 3D open‑world or fighting games remain a longer‑term goal, as the technology must first master more predictable environments before tackling high‑speed, complex interactions. Deslandes adds that the partnership is still in a pilot phase, with early adopters testing the platform on modest projects.
"The reality is that it’s a journey," he says. "If you are at the table now with us and maybe doing some pilots, then you will see where this tech goes." ### The sliding‑scale view of AI Both executives stress that AI should be seen as a sliding scale rather than a binary switch.
"There will always be a human in the loop—that’s our ethos," McBride affirms. The AI acts as an assistant that reduces human error, speeds up repetitive testing, and surfaces insights that might otherwise be missed. Yet final judgement, especially when distinguishing a bug from an intended feature, still rests with experienced QA professionals. In summary, the Side‑Modl.ai collaboration aims to fuse robust human testing knowledge with sophisticated AI analysis to create a more efficient, scalable, and rewarding QA workflow.
By automating low‑value tasks, empowering testers to become AI engineers, and maintaining a human‑centric oversight model, the partnership hopes to set a new standard for quality assurance in the gaming industry.