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Loqalit vs manual LQA
Manual LQA gives you human judgment on a sample of your product. Loqalit gives you automated coverage of all of it. Most teams need both, the question is which one should be doing the sweeping.
Where each approach wins
Manual LQA | Loqalit | |
|---|---|---|
Coverage | A sample, typically key flows in key locales | Every scanned page, every locale, every breakpoint |
Turnaround | Days to weeks per cycle | Minutes per scan |
Cost scaling | Linear with locales x screens x frequency | Flat plan by seats and scan volume, adding a locale adds no cost |
Layout & overflow errors | Only if the reviewer opens that exact screen at that viewport | Detected systematically at the DOM level |
Brand voice & creative nuance | Strong, human judgment | Limited, flags candidates, does not replace judgment |
Legal & regulatory copy | Required | Not a substitute |
Consistency across reviewers | Varies, severity assignment is subjective | Consistent scoring rules applied uniformly |
Continuous monitoring | Impractical | Native |
The real limitation of manual LQA is not quality, it’s arithmetic
Twelve locales x forty screens x three breakpoints is 1,440 states. A reviewer covering one state per minute needs three full working days per pass, per release. No team does that, so every manual LQA program is a sampling program whether or not it is described as one.
That is fine for the errors that appear everywhere. It is structurally bad at the errors that appear in one place, which is precisely where layout and context defects live.
The cost curve is the whole argument
Manual LQA cost is a function of locales x screens x review frequency. Every language you add multiplies the hours. Every release multiplies them again.
Loqalit is priced by seats and scan volume, not by language count. Adding your twelfth locale to a scan costs nothing extra, the same page is being rendered and evaluated either way. That is the structural difference: one model scales with your ambition, the other scales against it.
The honest case for keeping humans
Loqalit does not read your brand guidelines and decide whether a headline lands. It does not assess whether a legal disclaimer is enforceable in a given jurisdiction. It does not make a marketing tagline sing in Japanese.
The right split is usually: automation for coverage, humans for judgment. Let the scan sweep 1,440 states and surface the 30 that are broken; let your reviewers spend their hours on the copy where nuance actually determines the outcome, instead of on clicking through screens that were fine.
Frequently asked questions
Does Loqalit replace manual LQA entirely?
No. Loqalit replaces the manual sweep, the hours spent clicking through every locale looking for text that does not fit. It does not replace human judgment on brand voice, creative copy, or legal and regulatory language, which still require a native reviewer. The practical split is automation for coverage, humans for judgment.
Why can’t manual LQA cover everything?
It is arithmetic rather than diligence. Twelve locales across forty screens at three breakpoints is 1,440 states. At one state per minute that is three full working days per release, per pass. No team sustains that, so every manual LQA program becomes a sampling program, and the layout and context defects it misses are exactly the ones that appear in only one place.
What kinds of errors does manual review typically miss?
Errors that only exist once the page is rendered: text overflow, truncation, clipped buttons, overlapping elements, horizontal scroll, and RTL mirroring problems. A reviewer working from a bilingual export never sees the interface, so these pass review every time even when the translation itself is correct.