3-week translation cycles were killing our sprint velocity. Every merge meant untangling JSON files.

Localization is still the step that delays your releases.

The pattern is the same across most enterprise engineering organizations. A developer writes a new feature in English. Someone extracts the strings. They go into a TMS or an agency workflow. Translations come back one to three weeks later — after the sprint is already closed, after the feature was already shipped in English only, after the international release date has already slipped.

The teams that have integrated MT post-editing into the pipeline are often no faster: the translators spend as long correcting stylistically wrong MT output as they would have spent translating from scratch. The efficiency gain evaporated somewhere between the generic MT engine and the client's brand voice requirements.

And then there are the merge conflicts. The TMS bot creates a pull request for translated strings. The developer creates a feature branch that touches the same localization file. Both open at the same time. The three-way merge conflict stops the sprint.

The localization pipeline runs automatically when developers push code.

The Flixu GitHub App watches your repository. When a developer pushes new strings to a monitored file path, the app detects the changes, runs the translation pipeline with your configured Glossary, Brand Voice, and Translation Memory applied, and commits the translated files to a dedicated localization branch.

That branch is structurally separate from your feature branches. The TMS bot and the development workflow never write to the same files simultaneously. The three-way merge conflicts that stop sprints don't happen here.

For enterprise teams that ship weekly or more frequently, eliminating localization from the critical path means the German version of a feature ships when the English version ships — not two sprints later.

→ GitHub App setup and configuration: GitHub Integration

Your glossary is enforced on every string, not just the ones a reviewer catches.

An enterprise product with thousands of UI strings, updated across dozens of sprints, accumulates terminology decisions. Without systematic enforcement, "Dashboard" appears as three different German words in different parts of the interface. "Cancellation" appears in two variants in the same user account flow. The inconsistency is visible to users in international markets — and it signals that the product wasn't built for them.

In Flixu, the glossary is loaded as a payload constraint before the language model generates output. The approved term is specified before inference begins — the model builds the surrounding grammar around the fixed term, not the other way around. For inflected languages like German, Polish, or Japanese, this produces natural constructions rather than awkward insertions.

When a client's terminology is updated, every subsequent translation for that client applies the updated constraint — no PM email required, no risk that the freelancer who got the update email applied it to some strings but not others.

Teams using pre-translation glossary enforcement typically find terminology inconsistency drops from 15–25% of reviewed strings to under 2%.

→ Glossary enforcement in detail: Glossary Enforcement

Different departments need different brand voices — configured, not briefed.

An enterprise localization program spans multiple departments: marketing uses a casual, warm register; legal uses formal and precise; technical documentation uses neutral and domain-specific. When all of these run through external agencies or generic MT without configured brand voice, the output is inconsistent across the organization — the same end user encounters different tone as they move from a landing page to a checkout flow to a support article.

The Brand Voice Manager stores tone configuration per project or client profile. When the marketing team's content runs through the pipeline, it applies the marketing brand voice — formality level, stylistic preferences, phrasing constraints. When the legal team's content runs, it applies the legal profile. The configuration is set once; every translation request after that applies it automatically.

This replaces the briefing document model — where brand voice consistency depends on whether the assigned translator read and applied the PDF this week.

→ Brand voice configuration: Brand Voice Manager

LQA scores give you auditable quality data without manual review of every string.

Enterprise clients in regulated industries — legal, healthcare, financial services — are asking for QA documentation that manual workflows can't produce: which segments were reviewed, what quality score did they receive, was the glossary consistently applied. Without a structured QA pipeline, the answer is "we reviewed it internally."

Flixu's LQA scores every segment across five dimensions — Grammar, Accuracy, Terminology Consistency, Formatting, and Fluency — automatically on every project. Segments above the threshold are auto-approved and logged with their score and the approval timestamp. Segments below are routed to the review queue with the specific failing dimension marked.

That data exists without a manual reporting step. For enterprise teams that need to deliver quality documentation alongside translation deliverables, the audit trail is a byproduct of the pipeline, not a separate effort.

→ LQA and quality reporting: LQA & Quality Assurance

How Flixu fits into an enterprise localization program.

How the approach compares.