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Visual Testing

PQA Visual
Testing

Automated quality assurance with real browsers. Define test sequences, run them against live applications, capture screenshots at each step, and let AI detect what changed.

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Quality Assurance, Automated

Define your tests once. Run them on every build. Catch regressions before your users do.

Define Test Sequences

Describe your test flows in structured JSON. Each step specifies a browser action — navigate, click, fill, wait, screenshot — with clear pass/fail criteria.

Execute Against Real Browsers

Tests run in actual Chromium browser sessions via Metal Browser Automation. Not simulated, not mocked — real browser rendering.

AI Analyzes the Results

Captured screenshots are sent to Vision Model Analysis for structured verdicts — pass, fail, or needs review, with specific reasons.

How It Works

1

Define Test

Describe steps and expected outcomes in JSON

2

Browser Executes

Real Chromium runs each step sequentially

3

Screenshots Captured

Visual state recorded at each checkpoint

4

AI Analyzes

Vision models evaluate against criteria

5

Report Generated

Structured results with pass/fail verdicts

Testing Capabilities

From sprint validation to continuous visual regression detection

UAT Workflows

Automate user acceptance testing with repeatable, documented test sequences that anyone on the team can understand.

Sprint Validation

Run your test suite at the end of each sprint. Verify that new work did not break existing functionality.

Visual Regression

Before/after screenshot comparison powered by AI. Detects layout shifts, missing elements, color changes, and broken styling.

CI/CD Integration

Plug PQA into your deployment pipeline. Run tests automatically on every push, merge, or release.

Why AI-Powered Testing?

Traditional visual testing relies on pixel-perfect comparisons. A single font rendering difference or anti-aliasing change triggers a false positive. AI-powered analysis understands what it is looking at.

  • Fewer false positives — AI distinguishes meaningful visual changes from rendering noise
  • Semantic understanding — Knows that a button moved vs. a button disappeared are different severity levels
  • Natural language verdicts — Results explain what changed and why it matters, not just "pixels differ"

Traditional vs. PQA

Pixel Diff Testing High false positive rate
PQA + Vision AI Contextual analysis

False positive rates are illustrative. Actual rates depend on your application and test configuration.

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Stop Shipping Visual Regressions

See PQA Visual Testing catch real issues in a live demo.

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