Side, a veteran services provider formerly known as PTW, has entered into a six‑month memorandum of understanding (MOU) with Danish artificial‑intelligence specialist Modl.ai to co‑develop an AI‑driven quality‑assurance (QA) platform for the gaming industry. The agreement binds the two firms to collaborate on a system that blends Side’s deep‑rooted QA experience with Modl.ai’s cutting‑edge AI capabilities, aiming to create a more efficient, scalable, and human‑centric testing workflow. Modl.ai, founded in 2018, raised $1.7 million in seed capital two years later and has since focused on applying machine learning to game testing.
While earlier AI‑enabled QA tools required developers to ship entire builds or even share source code with the testing vendor, the new approach eliminates that friction. According to Modl.ai founder Christoffer Holmgard, the system works with the same product a tester would receive for manual testing – essentially a compiled game binary that runs on a device identical to the end‑user’s hardware.
The AI observes the game’s visual output, simulates player inputs, records every interaction, and then hands the resulting data set to a second AI model that flags anomalies, classifies them as potential bugs, and escalates the findings 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 reference material, then converse with the AI in natural language to teach it the game’s rules, mechanics, and intended behavior. Holmgard explains that this conversational interface lets teams describe gameplay concepts, illustrate edge cases with images, and supply narrative context, all of which enrich the AI’s internal model of the title.
Once the AI has a solid understanding of the game, developers specify the testing objectives. These can range from traditional regression test cases to novel scenarios that are deliberately crafted to exploit the AI’s pattern‑recognition strengths. Holmgard notes that human expertise is still crucial at this stage: testers must phrase prompts correctly, calibrate the AI’s expectations, and guide it toward the most valuable test paths.
The AI then executes the defined sessions, capturing screen recordings, log files, and other telemetry. The output includes concise bug reports, short video clips that serve as visual evidence, and comprehensive logs that are forwarded to a human QA specialist for validation.
One of the persistent challenges in automated testing is distinguishing a genuine defect from an intentional feature. The system addresses this by incorporating feedback loops from human testers.
When an expert labels a finding as a bug, a false positive, or an acceptable design choice, that information is fed back into the AI, gradually refining its decision‑making criteria. Over time, the model becomes attuned to the subtle nuances of a particular studio’s design philosophy, reducing the rate of misclassification.
Side sees this technology as a catalyst for career progression within its QA workforce. Chief Commercial Officer Martin McBride envisions QA professionals evolving into AI engineers, thereby moving up the value chain.
Instead of spending the bulk of their day running repetitive smoke tests, staff can focus on higher‑order activities such as strategic analysis, gameplay assessment, and creative test design. This shift not only enhances job satisfaction but also opens the possibility of repatriating QA roles to regions where they have previously migrated due to cost pressures. By upskilling employees, Side hopes to offer higher wages, new career ladders, and longer tenures. McBride emphasizes that AI is not a replacement for human testers but a tool that augments their capabilities.
He describes AI as a "sliding scale" where the proportion of automation can be adjusted based on project needs, yet a human will always remain in the loop. This philosophy aligns with the company’s broader ethos: technology should eliminate tedious, error‑prone tasks while preserving the critical judgment that only experienced QA specialists can provide. Modl.ai’s Chief Product Officer Benedikte Mikkelsen adds that many current tests are monotonous and can be safely automated, freeing humans to evaluate gameplay feel, narrative cohesion, and other qualitative aspects that machines struggle to quantify. She argues that the true value proposition of AI lies in reallocating human talent from repetitive execution to more insightful, creative testing activities.
When asked about the types of games the solution currently supports, Side’s Chief Operating Officer Oliver Deslandes cautions that the technology is best suited for titles with relatively simple mechanics—puzzle games, card games, and other experiences that do not demand rapid, high‑frequency action. Fast‑paced genres such as fighting games or expansive 3D open‑world adventures remain a longer‑term goal. Deslandes stresses that early adopters will likely be studios willing to pilot the system and accept its current limitations in exchange for cost savings and faster feedback cycles. In summary, the Side‑Modl.ai partnership aims to blend the reliability of seasoned human QA with the speed and scalability of AI.
By capturing game knowledge through natural‑language interaction, generating automated test runs, and feeding human‑validated feedback back into the learning loop, the joint solution aspires to reduce repetitive workload, improve bug detection accuracy, and create new professional pathways for QA talent. While the technology is still maturing and best applied to less complex titles, both companies view the collaboration as a stepping stone toward more sophisticated, AI‑enhanced quality assurance across the entire spectrum of video games.