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QA:AI

Most teams use AI as a coding assistant
We use it also as a Test Automation Engineer

Built on your automation standards, framework architecture, coding conventions, and quality rules. Faster, more consistent, easier to maintain.

Every automation contribution remains human-reviewed and fully governed.

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Automation Teams
Can't Keep Up

Product teams ship faster than automation teams can follow. The gap grows. Senior engineers drown in routine reviews. AI tools make it worse because they don't know your codebase.

What is broken today

Automation always falls behind delivery
Manual test design, repetitive implementation, limited capacity. Coverage gaps grow as delivery velocity accelerates.

Automation always falls behind delivery Manual test design, repetitive implementation, limited capacity. Coverage gaps grow as delivery velocity accelerates.

Senior engineers stuck in routine reviews
Coding standards, reusability violations, maintainability concerns are valuable engineering time spent on corrections instead of strategy.

Senior engineers stuck in routine reviews Coding standards, reusability violations, maintainability concerns are valuable engineering time spent on corrections instead of strategy.

AI tools don't know your organization
Generic AI generates code based on public knowledge. Without your context, output requires significant rework before it can be accepted.

AI tools don't know your organization Generic AI generates code based on public knowledge. Without your context, output requires significant rework before it can be accepted.

Automation knowledge stays tribal
Expertise lives in a small group of senior engineers. New team members learn through trial and error. Consistency suffers.

Automation knowledge stays tribal Expertise lives in a small group of senior engineers. New team members learn through trial and error. Consistency suffers.

Technical debt grows faster than coverage
Small deviations accumulate sprint after sprint. Automation becomes harder to maintain and less reliable as a quality safeguard.

Technical debt grows faster than coverage Small deviations accumulate sprint after sprint. Automation becomes harder to maintain and less reliable as a quality safeguard.

What our approach fixes

AI-Augmented Quality Engineering Framework

Enterprise Testing Context

We build a domain-aware knowledge layer using:

  • Requirements
  • User stories
  • Existing test assets
  • Automation framework standards
  • Coding guidelines
  • Historical defects

AI operates using your context, not generic internet knowledge. 

AI Test Design Agent

 Generates: 

  • Test scenarios
  • Acceptance test coverage
  • Edge cases
  • Risk-based test recommendations
  • Traceability mapping

AI Automation Agent

 Generates: 

  • Automation-ready test cases
  • Framework-compliant code
  • Reusable page objects
  • API test assets
  • Validation layers

All outputs follow predefined engineering standards.

AI Review Agent

Reviews:

  • Test coverage gaps
  • Automation code quality
  • Framework compliance
  • Naming conventions
  • Duplicate scenarios
  • Missing validations

Every output receives automated review before human approval.

Use Cases

Where it pays off first

Test Case Generation

Convert requirements and user stories into comprehensive test coverage.

Automation Development

Generate framework-compliant UI, API, and mobile automation assets.

Review Automation

Perform automated quality reviews before pull request approval.

Legacy Test Modernization

Convert manual test repositories into maintainable automation assets.

Release Readiness

Validate coverage, risks, and quality gates before production releases.

Metrics & Outcomes

Up to 10× faster feature test case development:
From spec to framework-compliant, reviewed automation.

What teams achieve:
  • Reduced review effort
  • Improved standards compliance
  • Higher test coverage consistency
  • Lower onboarding effort
  • Faster test data design

Supported Frameworks & Tools

Playwright (Web / API)
Appium (Mobile)
Karate (API)
k6 (Performance / Load)

FAQ

How is this different from using GitHub Copilot or Cursor for test automation?

Copilot writes code based on public knowledge. Our agents are fed your framework architecture, coding standards, and historical defects before they write a single line. The output follows your conventions, not a generic best guess.

Does it work with our existing automation framework?

Yes. We build on top of what you already have: Playwright, Appium, Karate, or k6. We don't replace your framework, we make it the standard the agents follow.

Who reviews the AI-generated code before it merges?

Every output goes through the "AI Review Agent" first: Coverage gaps, naming conventions, framework compliance, duplicates. Then a human engineer approves. Nothing merges without a human signature.

How does the AI know our standards and architecture?

We build a domain-aware knowledge layer from your existing assets: Requirements, user stories, existing test cases, framework docs, coding guidelines, historical defects. That becomes the agent's operating context.

Can it handle legacy test suites?

Yes. Legacy test modernization is one of the primary use cases: Converting manual test repositories into maintainable, framework-compliant automation assets.

How long does it take to get started?

Knowledge base build takes one to two weeks. Then the first cycle runs. No six-month integration.

Is the AI spend visible and controlled?

Every model run is auditable, every dollar attributed to its originating issue. You see exactly what each test case cost to generate.

Let's Work Together

We are happy to help you transform your DevOps infrastructure and accelerate your delivery pipeline.