Text overflow and truncation
Translated text that is longer than the source breaks layouts. fly.qc flags every instance before it ships.
Proprietary quality control
The QC system that catches problems before they reach your launch.
fly.qc is our in-house quality control platform. It runs checks across every language version in parallel, surfaces errors upstream in the pipeline, and is adding an AI-aware detection layer this year.
TAUS Innovator
Process Innovation94%
bugs caught upstream
~60%
industry average
Game marketing localization has a specific failure mode: translated content that passes linguistic review but breaks visually, technically or contextually when assembled. fly.qc is built to catch those failures, the ones that slip through LSP QC because they only appear in the final assembled asset.
Translated text that is longer than the source breaks layouts. fly.qc flags every instance before it ships.
Wrong font weight, missing glyph, incorrect line-height. Caught at the frame level, across all language variants.
Subtitles that run long, clip dialogue or exceed CPS limits are caught before the video is approved.
Platform and delivery specs checked automatically: resolution, bitrate, file size, codec. No last-minute surprises.
Logo safe zones, colour values, animation timings. Consistent across every market version.
Replaced voice-over checked against the visual edit for sync, level and duration before the file is delivered.
Questions
fly.qc is currently part of our integrated final-mile production service. We do not licence it as a standalone tool. If you are interested in the AI-aware layer early-access programme, get in touch.
LSP QC happens at the text level, checking the translation. fly.qc happens at the assembled-asset level, checking that the translated content renders correctly inside the final creative. They are complementary, not competing.
Yes. fly.qc is integrated into our production pipeline for video, image and audio assets. It has been built around the specific failure modes of game marketing localization.
The AI layer adds context-aware detection, understanding whether a localized visual communicates the same meaning in the target market. Rule-based QC catches technical errors; the AI layer catches semantic and cultural mismatches that rules cannot express.