Side, a long‑standing 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 venture that will blend Side’s deep QA expertise with Modl.ai’s emerging AI technology to create a hybrid, AI‑augmented quality‑assurance platform for video‑game developers.
Modl.ai, founded in 2018, is a relatively new player in the gaming sector. After securing $1.7 million in seed funding in 2020, the company has been refining its AI‑driven testing tools. According to Martin McBride, Side’s chief commercial officer, the MOU “effectively merges the strongest assets of Side and Modl.ai into a single, differentiated solution.” He adds that the gaming industry has long been searching for a reliable way to use AI to boost QA services, and the partnership aims to fill that gap. Earlier AI‑based QA approaches, including Modl.ai’s first‑generation product, required developers to hand over full builds—or even source code—so that the testing system could integrate via plugins and SDKs.
This method introduced considerable friction, as the founder, Christoffer Holmgard, explains, “became difficult over time.” The new framework eliminates the need for internal access. Instead, the game is delivered to the AI system exactly as a QA tester or end‑user would receive it. The AI runs the game on a comparable device, watches the screen, simulates inputs, and records a comprehensive data set of every action and visual change. A second AI layer then analyses this data, flags potential defects, and escalates the findings to a human reviewer for final validation.
The first phase of the workflow is what Modl.ai calls “building game knowledge.” Developers upload assets—textures, models, design documents, even screenshots—and converse with the AI in plain English. This natural‑language interaction helps the system form a contextual understanding of the title’s mechanics, narrative, and visual style. Holmgard notes that the process can involve a mixture of text, images, and other media, allowing the AI to develop a nuanced model of the game before testing begins.
Once the AI has a baseline understanding, developers specify the testing objectives. These can range from traditional test cases (e.g., verifying a menu opens correctly) to more exploratory scenarios that are tailored to the strengths of an AI‑driven approach. Holmgard stresses that human expertise is still essential at this stage: “You need to shape the prompts and language so the AI agent performs the right actions.” During a test run, the system automatically generates bug reports, complete with textual descriptions, short video clips (the “evidence”), and full log files.
These artefacts are then handed to a senior QA specialist who reviews the findings and decides how to act on them within the production pipeline. One of the persistent challenges in game QA is distinguishing genuine bugs from intended features.
According to Holmgard, feedback loops from human testers help the AI refine its judgment over time. “The AI learns from the prompts, the pre‑built game knowledge, and the ongoing feedback from testers who label each report as a bug, not a bug, or a feature with an explanation,” he says.
Side envisions a future where QA personnel evolve into AI engineers, shifting the skill set from repetitive manual testing to higher‑value activities such as prompt engineering, data analysis, and system optimisation. McBride explains that this transition “moves our people up the value chain,” allowing them to earn higher salaries, pursue new career paths, and potentially bring jobs back to regions where low‑cost testing labor has previously migrated away. By automating monotonous tasks, the AI frees testers to focus on strategic analysis, creative test design, and deeper gameplay evaluation.
Benedikte Mikkelsen, Modl.ai’s chief product officer, echoes this sentiment: many current tests are tedious and can be safely automated, freeing humans to assess gameplay feel, balance, and other artistic aspects that machines cannot yet grasp. She argues that the real value proposition of AI lies in handling repetitive flows while humans concentrate on more insightful, creative work. Both leaders are clear that AI will not replace QA staff. McBride points out that in certain low‑cost regions, the economics of testing remain tight, making a full AI takeover impractical.
Instead, the goal is to equip existing teams with tools that extend their careers and increase their impact. He describes AI as a “sliding scale” – a flexible augmentation where “there will always be a human in the loop,” which aligns with Side’s core ethos. When asked about the types of games the solution currently supports, COO Oliver Deslandes admits that the technology is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable, slower mechanics.
Fast‑paced genres such as fighting games or expansive 3D open worlds are still on the roadmap but require further development. Deslandes emphasizes that early adopters should be developers willing to experiment and iterate, acknowledging that the journey will involve pilots, refinements, and gradual expansion. In summary, the Side‑Modl.ai partnership aims to combine proven QA processes with cutting‑edge AI to create a more efficient, accurate, and human‑centric testing ecosystem.
By preserving the essential role of expert testers while leveraging AI to eliminate tedious work, the collaboration hopes to set a new standard for quality assurance in the gaming industry, offering both immediate productivity gains and a roadmap toward more sophisticated, AI‑enhanced development pipelines.