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AITAE
Most teams use AI as a coding assistant
We use it 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.
faster automation delivery
AI agents, one pipeline
human-reviewed output
generic AI, always your context
Automation teams can't keep up
Here is why, and what we did about it
What is broken today
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.
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.
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:
- RequirementsUser stories
- Existing test assets
- Automation framework standards
- Coding guidelines
- Historical defects
AI Test Design Agent
Generates:
- Test scenarios
- Acceptance test coverageEdge cases
- Risk-based test recommendations
- Traceability mapping
AI Automation Agent
Creates:
- Automation-ready test cases
- Framework-compliant code
- Reusable page objects
- API test assets
- Validation layers
AI Review Agent
Reviews:
- Test coverage gaps
- Automation code quality
- Framework compliance
- Naming conventions
- Duplicate scenarios
- Missing validations
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
base project setup
complex UI test cases created in a day
to fix a flaky UI test
knowledge base build
Outcomes
- Reduced review effort
- Improved standards compliance
- Higher test coverage consistency
- Lower onboarding effort
- Faster test data design
Supported Frameworks & Tools
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.
Is AIDLC viable for smaller teams?
We have a focused starter package for teams under 50 engineers. Less compliance scaffolding, same pipeline, faster start.