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Everything your team needs for linguistic & localization QA at AI speed

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Everything you need for productive localization QA:

Locale discoveryMulti-agent captureReview workbenchJira-ready evidenceAI bug detectionLive QA dashboardGTW IntelligenceSprint-ready reportsLocale discoveryMulti-agent captureReview workbenchJira-ready evidenceAI bug detectionLive QA dashboardGTW IntelligenceSprint-ready reportsLocale discoveryMulti-agent captureReview workbenchJira-ready evidenceAI bug detectionLive QA dashboardGTW IntelligenceSprint-ready reportsLocale discoveryMulti-agent captureReview workbenchJira-ready evidenceAI bug detectionLive QA dashboardGTW IntelligenceSprint-ready reports
50–70%
Faster release readiness
40–60%
Reduction in QA cost
80+
Languages supported
100%
Screenshot coverage (AI agent)

How localization scan works

From URL to full QA report — in minutes

Establish a localization baseline, monitor quality over time, and ship fixes faster — with AI-powered scanning, multi-agent capture, and a visual review workbench built for linguistic QA teams.

Check out localization scan

Localization Scan

Paste a URL · Discover locales · Capture screenshots · Get full report

1

Scan

Crawl & capture every locale

2

Detect

AI finds linguistic issues

3

Review

Triage in the workbench

4

Report

Ship sprint-ready plans

Localization scanner

Industry-grade scans, modeled like your product

  • Add any site URL and schedule recurring runs (weekly / daily).
  • Accessibility health alongside localization signals.
  • Issues, pages scanned, and locales — at a glance.
https://experienceleague.adobe.com
Make recurringWeeklyDaily

Every Monday · at 12:00 PM

Accessibility health
87/100
Good
Issues
12
Pages scanned
8
Locales
8

AI agent squad

Scout
Lingo
Pixel
Hawk
Activity log
✓ 15:34:10 Capturing overview / DE
✓ 15:34:10 Capturing overview / ES
✓ 15:34:10 Capturing overview / FR
⟳ 15:34:11 JA: Waiting for render

Review workbench

FR · DE · IT — side-by-side with EN source

Critical · Untranslated header→Submit to Jira

GTW² · Linguistic testing

The 10 biggest challenges in localization QA — solved by AI

Localization testing is broken. Teams depend on tribal knowledge, spend hours on manual screenshots, and still ship bugs in production. GTW2's AI agents eliminate each of these pain points.

Onboarding

Product & linguistic expertise dependency

Effective L10n QA requires deep product context plus native-level language judgment — a combination that's rare, expensive, and hard to scale across releases.

Impact

Slow onboarding, inconsistent testing quality across teams.

Linguistic depth

Linguistic expertise gap

General QA testers miss nuance: tone, register, broken plurals, and locale-specific UX that only fluent reviewers catch — but you can't staff every market.

Impact

Missed errors and weak UX in local markets.

Throughput

Manual screenshot & comparison fatigue

Capturing and eyeballing dozens of locales per release doesn't scale. Teams burn hours on repetitive work and still leave huge page × locale combinations untouched.

Impact

Burnout, bottlenecks, and shallow coverage before ship dates.

Coverage

Inconsistent coverage across locales

Sampling a few screens in a few languages gives a false sense of safety. The long tail of pages and markets is where costly escapes hide.

Impact

Production defects that reach users in untested combinations.

Discovery

Unknown locale surface area

Sites add paths, subdomains, and query-based locales constantly. Teams struggle to even know what to test before they can test it well.

GTW² AI solution

GTW² Scout crawls your domain, maps locale strategies, and keeps the capture set aligned with what's live.

Detection

Subtle linguistic defects at scale

Human review doesn't scale to thousands of screenshots per release, yet small wording issues erode trust in every market.

GTW² AI solution

Lingo + Hawk agents classify linguistic and visual issues with consistent criteria — so nothing depends on who was on shift that day.

Evidence

Weak audit trail for stakeholders

Spreadsheets and chat threads don't hold up for compliance, client reviews, or postmortems. Evidence needs to be tied to locale, screen, and severity.

GTW² AI solution

Every finding is logged with locale, severity, and screen evidence — exportable for legal and client-ready reports.

Workflow

Friction from triage to fix

Even when issues are found, handoffs to Jira and dev teams are slow, and retesting after fixes is often skipped.

GTW² AI solution

Pixel + Ops agents orchestrate capture and handoff; the workbench links evidence to Jira and supports structured retesting.

Velocity

QA latency blocking release trains

When L10n QA is manual, it becomes the gate — trains slip or ships go out with known risk.

GTW² AI solution

Automated scans complete in minutes, so teams get a full baseline every release without pushing dates.

Confidence

No single view of quality

Leads can't answer simple questions: Are we ready to ship? Which locales are red? What improved since last sprint?

GTW² AI solution

Atlas ties coverage, trends, and release readiness into one dashboard — same numbers for QA, PM, and client managers.

Complete feature set

Everything GTW2 includes

Every tool your localization QA team needs — scanning, agents, workbench, dashboard, bugs, reports, and integrations.

  • Multi-language screenshot capture
  • AI bug logging & triage
  • Sprint-ready fix plans
  • Live QA dashboard
  • Jira integrations
  • PDF scan reports
  • Role-based access
  • Scheduled & recurring scans

Localization ROI

82% of your localized pages ship untested

What's localization QA actually costing you?

Plug in your numbers. See the time, money, and risk you're burning — and what changes when GTW2's AI agents handle the work.

Configure your setup

Sliders update results live

Pages50
Locales12
Viewports2
Releases / month4
QA hourly rate ($)40
Per release
1,200checks
50p × 12l × 2v

Time per release

Compare manual QA hours to a GTW2 scan.

Manual QA
1.7d
GTW2
5.0m
Per release
1.7d
5.0m
Monthly
53h
20.0m
4 releases
Yearly
639h
240.0m

Coverage gap

What manual sampling misses vs full automation.

Manual~18%
984 of 1,200 combinations untested
GTW2100%
1,200 of 1,200 — every page, every locale

Production risk

Escaped defects from untested locale × page combinations.

Escaped defects / release
34bugs ship to prod
Cost per escaped bug
~$1,200triage + hotfix + deploy
Annual escaped defect cost
$2.0Mhidden cost
These are bugs in the 82% of page×locale combinations your team never looks at. They reach 12 markets simultaneously. GTW2 catches them before release.

Financial impact

Labor, headcount equivalence, and total savings.

Annual QA labor
$26k
$2k/yr
QA headcount equiv.
0.3 FTE
0FTE
Redeploy to functional QA
Total savings
$2.0M/yr
Labor + escaped defect cost

Release velocity

Delays removed when QA stops blocking the train.

L10n QA delay
1d
0days
Ship all markets same day
Annual delay saved
48days/yr
4 releases × 12 months
Regression coverage
0%
100%
Cross-release visual diffs

Detection accuracy

Human fatigue vs consistent AI coverage.

Human tester71%

Accuracy drops after hour 8

GTW2 · Hawk agent97%

Consistent across 1 or 100,000 screenshots

Replace 1.7d of manual work with a 5.0m scan

1,200 screenshots · 12 locales · 100% coverage · every release

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Join 500+ teams who've automated their entire localization QA workflow. Scan your site free — results in under 3 minutes. No credit card required.

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Learn about the category

What is agentic localization QA — and why it’s replacing manual l10n testing.

A deep dive on autonomous localization testing, how it differs from visual regression and TMS workflows, and when it’s the right fit for your team.

Read the guide

GTW2

Ship every locale with confidence — not guesswork.

GTW2 combines AI-driven crawling, multi-language capture, and a visual review workbench so your team can baseline quality, catch regressions, and prove coverage to clients and compliance — without scaling headcount linearly with every new market.

Built for serious localization QA