SaaS Localization Workflow Demo
A live integration demo for VP Product and engineering leads at SaaS companies. No platform lock-in, no forced migration. We connect to your existing stack and show you the workflow running on your actual tools.
See what breaks in most workflows ↓Localization friction compounds silently. Each of these issues adds days to your release cycle and hours to your engineering week. Most teams accept them as unavoidable until they see the alternative.
Your TMS vendor forces you onto their platform. Switching means migrating TMs, glossaries, and workflows. You are paying for features you do not use and locked out of ones you need. The switching cost grows every quarter, which is by design.
Average TMS migration: 3 to 6 monthsEngineers should not need to learn a translation platform. String files should flow from GitHub or GitLab to translation and back via PR. Most providers require manual export/import cycles that pull developers out of their tools and into browser-based portals.
Manual handoff: 2 to 4 hours per sprintSprints move fast. Localization cannot be a 2-week waterfall bolt-on at the end. Continuous delivery requires continuous localization — strings translated as they are merged, not batched at the end of a release. Batching adds 1 to 3 weeks of lag per localized release.
Batch loc adds 1 to 3 weeks per releaseTranslators see strings without UI context. "Cancel" could be a button label or a subscription action. Without screenshots, usage notes, or component references, translations are educated guesses. The result: revision cycles that eat into your next sprint.
Ambiguous strings: up to 30% revision rateStatus updates in one tool, files in another, communication in a third. Your PM spends more time coordinating the localization vendor than managing product. Translation status lives outside your project management system, invisible to sprint planning.
PM coordination: 5+ hours/week averageHow you integrate localization into your dev workflow determines whether it accelerates or blocks your release cadence. Here is how the three common models compare.
Everything runs through the vendor's portal. Dev team needs training and credentials. Integration means connecting your repo to their system on their terms. Rush fees for anything outside the standard flow. Updates and roadmap are driven by the vendor's priorities, not yours.
Feature bloat increases cost. You pay for machine learning modules and analytics dashboards your team never opens. Switching cost compounds quarterly as your TMs and glossaries grow inside their proprietary format.
You hire translators directly and wire together scripts, spreadsheets, and Slack channels. Quality varies by individual. No shared translation memory across translators. When a freelancer is unavailable, their context leaves with them.
You are the project manager, the QA team, and the escalation path. This works at low volume. It collapses when you add your third language or ship more than once a month.
Integrates into your existing tools: GitHub PRs for string delivery, Slack for communication, your TMS for translation memory. No new platforms to learn, no credentials to manage, no portals to check. Strings flow from your repo to translation and back as a pull request.
Same team learns your product context over years, not months. Localization becomes part of your sprint, not a separate project. Status updates appear in your existing channels. <60s median request-to-production time to date.
Tech-agnostic integration that works with any TMS (Phrase, Trados, MemoQ), any repo (GitHub, GitLab), and any chat tool (Slack, Teams). Kobalt adapts to your tools, not the inverse.
A developer pushes new UI strings to a feature branch on GitHub. A webhook detects the change and routes the strings to Kobalt. The translation team — the same people who have been working on your product for years — translates with full product context from your TMS (Phrase, Trados, or whichever you use).
Completed translations are delivered back as a pull request to the same branch. Status updates post to your Slack channel. Your PM sees progress in Jira without leaving their workflow. No file exports, no portal logins, no manual coordination.
GitHub / GitLab Phrase / Trados / MemoQ Slack / TeamsKobalt processes over 80,000 translation requests per month across all clients to date. Median time from request to production is under 60 seconds for automated pipeline requests. 98.7% on-time delivery rate across all content types to date.
Team continuity is what makes this sustainable. The same linguists learn your product terminology, your tone, and your UI patterns. They do not need a style guide refresher every quarter because they have been translating your product for years.
80K+ requests/month <60s median 98.7% on-timeFrom stack audit to continuous localization in four weeks. No forced migrations, no new tools to learn.
We audit your current tools — TMS, repo, chat, PM tool — and map your content flow end to end. No assumptions about what you "should" use. The output is an integration architecture doc showing exactly how localization plugs into your existing workflow, with specific webhook endpoints, API connections, and channel configurations.
Technical connections deployed: webhooks, API links, channel setup. A pilot batch of real strings goes through the full workflow — from your repo to translation to pull request. Your developers and PMs experience the actual process, not a sales demo. Issues are identified and resolved on live content.
Localization runs as part of your sprint. New strings flow from repo to translation to PR automatically. Status updates appear in your chat tool. No manual coordination, no separate project management. Adding a new language means updating a config file, not rebuilding a workflow.
Book a demo showing how localization fits your CI/CD workflow.
Book a Dev Workflow Integration DemoNo. Kobalt integrates with your existing TMS — Phrase, Trados, MemoQ, Lokalise, or any other platform. Your translation memories, glossaries, and workflows stay where they are. We connect to your system via API or file exchange, whichever your TMS supports. No migration, no data loss, no retraining your team.
We use a combination of screenshot references, developer comments in string files, and a shared context database that builds over time. When your developers add context comments to string keys (e.g., "Cancel button on subscription page"), that context flows to translators automatically. For ambiguous strings, we flag them in Slack before translating rather than guessing.
JSON, YAML, .properties, XLIFF, .strings (iOS), .xml (Android), .arb (Flutter), .po/.pot (gettext), .ts (Qt), .resx (.NET), and CSV. If your format is not on this list, ask — we have handled virtually every localization file format in production. The format is parsed and reconstructed programmatically, so structure and key ordering are preserved exactly.
Kobalt processes 80,000+ requests per month with a median request-to-production time under 60 seconds to date. For continuous localization, new strings pushed to your repo are picked up, translated, and returned via PR within the same sprint. Batch sizes of 10 to 500 strings are typical per sprint cycle. Larger launches are scheduled with advance notice, same as any sprint dependency.
A webhook on your repo monitors designated string files for changes. When a developer pushes new or modified strings to a branch, the webhook triggers an extraction job. Strings are routed to translation, and completed translations are delivered back as a pull request to the same branch. Your team reviews and merges like any other PR. No manual export, no file uploads, no separate platform to check.
Adding a new language means translating your existing string base (the backlog) plus including the new locale in ongoing workflows. The backlog is scoped and quoted separately — typically 2 to 4 weeks depending on volume. Once the backlog is complete, the new language receives all future strings automatically through the same integration pipeline. No configuration changes on your side beyond adding the locale to your build.
Yes. We can generate pseudolocalized string files that simulate text expansion (typically 30 to 40% longer than English), include accented characters for encoding validation, and wrap strings in brackets for visual identification of untranslated content. These files are delivered through the same PR workflow so your QA team can test layout before real translations arrive.
When string keys include max-length metadata (common in .xml and XLIFF formats), translators see the constraint and work within it. For formats without built-in constraints, we maintain a shared constraint file that maps keys to maximum character or pixel widths. Translations that exceed constraints are flagged during QA and reworked before delivery. This is especially critical for mobile UI, navigation labels, and button text.
Tell us your stack (TMS, repo, chat tool). We will show you a live integration example with your actual tool combination — no slides, no generic demo.
Prefer email? ricard@kobaltlanguages.com