Side, a seasoned services provider, 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 sector. The agreement binds the two parties to collaborate on a system that blends Side’s extensive QA expertise with Modl.ai’s cutting‑edge AI capabilities, aiming to create a more efficient, less repetitive testing process while still keeping human insight at the core. ### Background of the Partners Side, previously known as PTW, has been a fixture in the outsourced services arena for many years, offering a range of testing, localisation, and support services to game developers worldwide. Modl.ai, on the other hand, is a relatively new entrant that launched in 2018 and secured $1.7 million in seed funding two years later.
The Danish startup has focused on applying machine‑learning techniques to the QA workflow, but its early offerings required developers to hand over full builds or even source code, creating friction and limiting adoption. ### The New Vision Martin McBride, Side’s chief commercial officer, describes the partnership as “bringing together the best of Side and the best of Modl.ai into a unique solution.” He adds that the gaming industry has been searching for a "holy‑grail" AI‑augmented QA system that actually reduces manual effort without sacrificing accuracy. According to McBride, the new joint solution could finally fill that gap. ### How the Technology Works In previous iterations, Modl.ai’s AI needed an integration plug‑in and SDK, and developers had to upload the entire code base so the system could analyse it.
That approach proved cumbersome and, as founder Christoffer Holmgard notes, “became difficult over time.” The revamped system eliminates the need for internal access. Instead, developers provide the product exactly as they would hand it to a human tester or eventual consumer—typically a compiled build installed on a device that mirrors the target platform. Once the build is supplied, the AI runs the game, monitors the screen, simulates player inputs, and records every interaction.
The data stream is then fed to a second AI layer that scans for anomalies, flags potential bugs, and escalates findings to a human reviewer. Holmgard calls the initial data‑gathering phase "building game knowledge." Developers can upload assets, design documents, screenshots, and even converse with the AI in natural language to teach it the context and mechanics of their title. After the knowledge base is established, the development team defines test objectives. These can range from classic regression test cases to novel scenarios that exploit the AI’s pattern‑recognition strengths.
Holmgard stresses that human expertise is still required to craft effective prompts and guide the AI toward the right behaviours. The AI then executes the defined sessions, producing bug reports that include textual descriptions, short video clips as evidence, and comprehensive log files. These reports are handed over to seasoned QA specialists.
The specialists validate the findings, decide on severity, and provide feedback such as “this is a bug,” “this is intended behaviour,” or “this is a design choice.” Over time, this feedback loop refines the AI’s decision‑making, allowing it to better distinguish between genuine defects and intentional features. ### Human‑Centric AI and Career Progression Side sees the technology as a catalyst for upskilling its workforce. Rather than keeping staff locked in repetitive, low‑value testing tasks, the company envisions QA professionals evolving into AI engineers who design prompts, curate training data, and interpret AI‑generated insights.
McBride explains that this shift not only raises salaries but also opens new career pathways, potentially repatriating jobs to regions where they had previously migrated due to cost pressures. The AI also promises a more fulfilling day‑to‑day experience for testers. By automating routine smoke tests and other monotonous checks, testers can focus on strategic analysis, creative exploration of gameplay, and higher‑level problem‑solving.
Benedikte Mikkelsen, Modl.ai’s chief product officer, notes that eliminating repetitive flows frees humans to assess game feel, balance, and emergent behaviours—tasks that truly benefit from human intuition. ### Limits and Future Roadmap Side’s chief operating officer Oliver Deslandes cautions that the current solution is best suited for simpler titles—puzzle games, card games, and other experiences with repeatable mechanics. Fast‑paced, 3‑D open‑world or fighting games present challenges that the system will need more development to handle. Deslandes frames the partnership as a pilot phase: early adopters willing to experiment will help shape the technology’s evolution, while more demanding studios may need to wait for later iterations.
### The Sliding Scale Ethos Both companies agree that AI will never fully replace human QA. McBride describes AI as a "sliding scale" where the proportion of automation can be adjusted, but a human will always remain "in the loop." This philosophy underpins the tool’s design: AI handles the heavy‑lifting of data collection and initial bug spotting, while humans provide the nuanced judgment that only experience can deliver. By reducing human error and fatigue, the combined approach aims to raise overall quality while keeping the workforce engaged and valued. ### Conclusion The Side‑Modl.ai MOU represents a strategic attempt to merge deep industry QA knowledge with sophisticated machine‑learning techniques.
By delivering a system that works with compiled builds, learns through natural‑language interaction, and continuously improves via human feedback, the partnership hopes to set a new standard for AI‑augmented quality assurance in gaming. While the technology is presently focused on less complex titles, the roadmap points toward broader applicability as the AI matures. Ultimately, the collaboration seeks to empower QA professionals, enhance game quality, and demonstrate that AI, when used responsibly, can be a powerful ally rather than a replacement for human expertise.