The problem
You ship in 15 languages but you test in one. What slips through is not just a mistranslation. It's the product breaking when translated:
- Truncated buttons and overflowing layouts in German and Finnish
- Untranslated strings and raw i18n keys leaking to production
- RTL layouts that mirror the design but break the flow
- Locale-specific logic (address forms, payment methods, date and number formats) that nobody in HQ ever exercises
- Machine-translated content that reads fine in the TMS but is wrong in context
Crowd-testing vendors attack this with panels of human testers: days or weeks of turnaround, coverage that depends on who picked up the job, and reports you cannot re-run.
How Donobu runs your LQA
One integrated system.
Our engineers author your suite.
Forward-deployed engineers in test (FDETs) spend a few days writing and reviewing a localization regression suite built on custom plugins for your localization guidelines: CLDR locale rules for dates, numbers, currency, and plurals; your terminology; your markets. For translation quality we use AI checks or hand assets to your existing provider (Smartling, Lokalise, Translated, and similar). We test on top of your localization stack, not instead of it.
AI agents do about 90% of the work.
They run your real user journeys (signup, checkout, search, settings) in every locale you ship, on every release, with consistent verification instead of tester-to-tester variability.
Engineers own the outcome.
Where AI is not enough, FDETs take over. SDETs review all final results. You never receive unreviewed AI output.
Findings arrive as evidence.
Screenshots of the failure state, the exact locale and step, and replayable Playwright tests, so a fix is verifiable rather than a matter of opinion.
Everything runs inside your environment.
Scripts, not contract testers. Your pre-release product and data are never exposed to a crowd of outside testers.
Proof
Coursera uses Donobu to QA content before launch, paying for outcomes rather than tester-hours.
A global marketplace releasing across dozens of markets: our engineers authored a localization regression suite in two days. It now runs in about four hours. The same checks through a crowd-testing vendor took roughly five weeks per cycle.
Pricing
Per page per locale checked. Outcome-based, not tester-hours. That typically comes out to about half the cost of crowd-testing, and it is fully usage-based: test more when you launch more, less when you do not.
Why teams switch
From crowd-testing panels
Weeks become hours, sampled locales become all of them, portal tickets become replayable tests.
See the full comparison with ApplauseFrom in-house
Your engineers do not speak 15 languages, and a locale QA hire per market does not scale. We already built the team and the system.
From TMS string checks
Phrase and Crowdin style QA checks the translation. Donobu checks the product: strings in their real UI, flows in their real market configuration.