Test Automation Services
Tests that write themselves.
Fix themselves.
Run smarter.
AI has changed what test automation can be not just scripts that verify, but systems that learn your codebase, predict where failures hide, and heal when things change.
Our Decade long experience, validated in numbers
Enterprise Customers
The shift
What changes when AI.
is part of the test suite.
Without AI automation
- Tests written manually, one by one
- Selectors break on every UI release
- Full suite runs on every commit slow feedback
- Visual regression caught by humans in review
- Flaky tests silently erode team trust
- Coverage gaps discovered after production bugs
With AI-powered automation
- AI generates tests from user stories and diffs
- Self-healing selectors repair without intervention
- Predictive selection runs only the relevant tests
- Visual AI catches pixel-level regressions automatically
- AI detects and quarantines flaky tests at source
- Coverage gaps surfaced by risk model, before bugs reach users
Trusted by Leading Enterprises
AI capabilities
Six ways AI makes your test suite smarter.
Autonomous test generation
LLMs parse your user stories, API schemas, and code diffs to write test cases covering happy paths, edge cases, and negative flows without a human scripting each one.
70%
Reduction in manual test authoring time
Self-healing selectors
When a UI element changes, the AI identifies the new locator and repairs the test automatically. Maintenance stops being the reason automation suites get abandoned.
~0
Broken tests from routine UI changes
Predictive test selection
A model trained on your codebase's change patterns and defect history selects the tests most likely to catch a failure for any given commit skipping the rest.
4×
Faster feedback loops in CI/CD
Intelligent visual regression
AI-based comparison understands what a real visual regression looks like versus a rendering difference fewer false positives, more genuine signal across browsers and viewports.
90%
Drop in visual test false positives
Flake detection & root-cause
AI analysis of test result history identifies flaky tests, intermittent failures, and recurring root causes converting noise into a clear backlog of fixes.
3×
Faster mean time to diagnose failures
AI-driven coverage gap analysis
Static analysis combined with a risk model maps untested code paths and surfaces the ones most likely to cause production failures prioritised, not just enumerated.
+40%
Coverage increase in first 8 weeks
Reference pipeline
Where automation lives in the delivery flow.
An abstract reference AI intelligence woven across every gate, not bolted on at the end.
RestAssured·Pact
Appium · Visual AI
JMeter · Artillery
Burp · Checkmarx
Production signals retrain the AI — smarter with every release
Tool ecosystem
The complete toolchain.
- Playwright
- Cypress
- Selenium
- WebdriverIO
- Puppeteer
- TestCafe
- Nightwatch.js
- Cucumber
- pytest
- SpecFlow
- Behave
- JUnit 5
- TestNG
- Jest / Mocha
- Robot Framework
- Postman
- RestAssured
- Karate
- Pact
- WireMock
- SoapUI
- Newman
- Hoverfly
- k6
- Gatling
- JMeter
- Locust
- Artillery
- BlazeMeter
- Lighthouse
- WebPageTest
- OWASP ZAP
- Snyk
- Burp Suite
- Checkmarx
- SonarQube
- Semgrep
- Trivy
- GitLeaks
- Appium
- BrowserStack
- Espresso
- XCUITest
- Detox
- Sauce Labs
- LambdaTest
- AWS Device Farm
- Applitools
- Percy
- Axe
- BackstopJS
- WAVE
- Pa11y
- Deque
- Mabl
- Testim
- Launchable
- Functionize
- Healenium
- Diffblue
- Applitools UFG
- Copilot for Tests
- Allure
- GitHub Actions
- TestRail
- Zephyr Scale
- ReportPortal
- Jenkins
- GitLab CI
- Azure DevOps
How we work
From no automation to a
self-improving test suite.
Assess
Audit current coverage, tooling, and the highest-risk gaps. Agree on scope before a line is written.
Architect
Design the framework structure, CI/CD integration, and coverage model before scripting begins.
Build & Integrate
Deliver the automation suite in sprints alongside active development—not after it. Reports live on day one.
Augment with AI
Layer in generation, self-healing, and predictive selection. The suite gets smarter as it accumulates data.
Everything you need to know
AI-powered test automation goes beyond scripts that simply verify a fixed set of steps. It uses AI to generate test cases from user stories and code diffs, self-heal broken selectors when the UI changes, predict which tests are most likely to catch a failure for a given commit, and automatically detect flaky tests. The difference from traditional automation is that the suite improves over time as it accumulates data from real test runs, rather than staying static until someone manually updates it.
AI-driven test generation, which parses user stories, API schemas, and code diffs to write test cases covering happy paths, edge cases, and negative flows, typically reduces manual test authoring time by around 70%. It doesn't eliminate human involvement entirely: someone still reviews and refines AI-generated tests, but the starting point is generated rather than written from a blank file.
The most common reason automated test suites get abandoned is maintenance overhead: as the UI changes, selectors break, and someone has to manually update every affected test just to keep the suite passing. Self-healing selectors solve this by having AI identify the new locator when a UI element changes and repair the test automatically, which is why teams using this approach see close to zero broken tests from routine UI changes.
No. Predictive test selection uses a model trained on a codebase's change patterns and defect history to identify which tests are most likely to catch a failure for a specific commit, running only those instead of the full suite every time. This typically produces around 4x faster feedback loops in CI/CD, since a full test suite that takes 20 minutes to run becomes a bottleneck when a team ships multiple times a day.
AI-based visual comparison is trained to distinguish an actual visual regression from a harmless rendering difference between browsers or viewports, rather than flagging every pixel-level change as a failure. This typically produces around a 90% drop in visual test false positives compared to pixel-diff tools, which means fewer test failures that require a human to manually confirm whether it's a real bug or not.
AI analysis of test result history identifies which tests are flaky, what pattern of intermittent failures they show, and what root cause is behind recurring failures, turning inconsistent test noise into a prioritized backlog of fixes. This is different from the common workaround of simply re-running a flaky test until it passes, which hides the underlying problem instead of fixing it and gradually erodes trust in the test suite's results.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
Connect With us
Ready to make your test suite learn?
Tell us about your current coverage and we’ll walk through the AI automation architecture together.