Our localization is always on the critical path to launch. Flash sales go live in English and reach Germany 48 hours later.

Generic MT doesn't know your brand voice. You spend more time fixing than translating.

A consumer app has a voice. It's warm, or energetic, or direct — specific enough that users in your primary market feel it in every push notification and every in-app message. Generic MT translates that voice into the statistical average of your target language. The Brazilian Portuguese version of a Gen-Z loyalty campaign comes back formal. The German flash sale sounds like a corporate announcement. The fix is manual, and it takes as long as the translation did.

Meanwhile, the 48-hour TMS turnaround for a flash sale update with 500 strings means the English campaign is live, converting, and building social proof before the localized version even starts review. For time-sensitive promotions, that gap is direct revenue lost.

From the community:

The fix has two parts: brand voice loaded before generation, and quality scoring that lets you approve without reading every string.

Auto-Approval means high-confidence translations ship without a manual review step.

The review bottleneck in a high-volume mobile localization workflow isn't caused by bad translations. It's caused by routing every translation through human review regardless of quality. A push notification with a 97 LQA score and a 99% Translation Memory match doesn't need a reviewer — but it still sits in the queue alongside the three strings that do.

LQA scores every segment automatically across five dimensions: Grammar, Accuracy, Terminology Consistency, Formatting, and Fluency. Segments that score above 90, or match Translation Memory at 99%, are auto-approved and deployable immediately. Only the segments below threshold appear in the review queue — with the specific failing dimension flagged.

For a team translating flash sale content daily, this changes the economics: the review effort concentrates on the edge cases. Standard promotional copy, push notification text, and UI strings with established patterns auto-approve. Localization stops being a blocker for same-day campaigns.

According to CSA Research, 76% of consumers prefer to buy in their native language — but only if the localized version is available when the campaign runs.

→ Auto-approval workflow details: Auto-Approval Workflows

Your brand voice is in the configuration — it doesn't depend on which translator read your style guide.

A consumer app brand voice is usually documented somewhere: a tone guide, a Figma annotation, a Notion doc that got written eighteen months ago. Whether that guide is being applied to every translated string today depends on who last read it and how carefully they remembered it under deadline pressure.

The Brand Voice Manager stores your consumer persona as a workspace configuration. Casual register, energetic phrasing, specific vocabulary preferences, formality level — defined once, applied to every translation request automatically. The Gen-Z loyalty campaign in Brazilian Portuguese gets the same enthusiastic, casual register as the English original because the configuration was present before the language model generated the first word.

For consumer apps where brand drift across markets reduces conversion rates measurably — industry patterns show brand-inconsistent localizations generating 20–30% lower click-through rates than properly calibrated ones — the consistency is a business metric, not just a quality preference.

→ Consumer brand voice configuration: Brand Voice Manager

Cultural adaptation runs automatically — currencies, formats, and regional conventions.

"Save 20%" doesn't localize by translating the words. The percentage format, the currency symbol, the date format in a "Limited offer until 05/06" — all of these mean different things in different markets. A German user reading a US-formatted date will pause. An Austrian user seeing an incorrect currency context will notice.

The Cultural Adaptation Engine handles format-level adaptation automatically alongside translation. Date formats convert to the target market's convention. Currency context adapts to regional expectations. Measurement units convert where applicable. No manual configuration per market, no post-translation review pass for format errors.

For consumer apps launching into multiple markets simultaneously, this eliminates a category of review corrections that currently consume QA time without adding translation value.

→ Cultural adaptation scope: Cultural Adaptation Engine

New app strings translate and commit to your repository when developers push them.

A new feature ships in English. The localization cycle starts: string extraction, TMS upload, translation queue, QA, engineering re-integration. By the time the German version is live, the feature has already been in production for a week in English-speaking markets. International users get a delayed, sometimes outdated version.

The Flixu GitHub App monitors your repository's localization file paths. When a developer pushes new or changed strings, the app detects them, runs the translation pipeline with your Brand Voice and Glossary applied, and commits the translated files to a dedicated localization branch — structurally separate from your feature branches to prevent the merge conflicts that stop sprints.

High-confidence strings auto-approve. Flagged strings appear in the review queue. The localized version of a feature can ship the same day as the English version, not a sprint later.

→ GitHub App for mobile CI/CD: GitHub Integration

Mobile format support.

For App Store and Google Play listing content — descriptions, keywords, release notes — these are typically stored as plain text or Markdown in a localization directory and work with the standard file format support.

Where Flixu fits — and where it doesn't.