AI testing suite background

AI-assisted Testing Suite — p7

Accelerate test creation, reduce flakiness, and validate complex user journeys across web and mobile using adaptive AI-driven test planners.

Request a demoPrivacy details

Overview

Our Ai-assisted Testing Suite (p7) combines model-guided test generation, visual regression, and runtime heuristics to keep delivery pipelines green. Designed for enterprise-grade apps, cloud-native services, and hybrid mobile/web platforms.

  • Smart test generation from user stories and API contracts
  • Self-healing selectors and ML-based flakiness detection
  • Integrates with CI/CD and popular test runners
Talk to an expert
Overview screenshot

Key features

Capabilities

  • Automated test generation from acceptance criteria
  • Visual diffing with tolerance profiles
  • Parallel execution & cloud runners

Security & compliance

Built with secure data handling for AUS clients — masking, audit logs, role-based access, and enterprise SSO integrations.

Generates prioritized test suites based on impact analysis and recent code changes — reduces run time while preserving coverage.

Combines DOM heuristics and visual anchors to recover from minor UI changes automatically.

Detailed flakiness metrics, time-to-detect regressions, and failure clustering to speed up root cause analysis.

Typical workflow

  1. Connect repo and CI
  2. Train test intents from stories or spec
  3. Run prioritized suites on pull request
  4. Review flakiness insights and remediate
Schedule trial
Workflow diagram

Integrations

Seamlessly plugs into your toolchain.

Works with GitHub Actions, GitLab CI, Bitbucket Pipelines, Azure DevOps and Jenkins.

Supports Playwright, Cypress, Selenium, Appium, and native unit-test integration for end-to-end orchestration.

Export metrics to Datadog, New Relic, or Prometheus for unified observability.

Case studies

Case study 1

Fintech platform

Reduced pipeline time by 62% with prioritized suites.

Case study 2

Retail mobile app

Flakiness dropped from 28% to 3% after self-healing selectors.

Case study 3

SaaS B2B dashboard

Automated regression saved 120 developer hours monthly.

Team highlight

Lead engineer

Dr. Maya Nguyen — Lead AI Engineer

Maya leads model development for test generation and failure triage. Background in ML systems and software reliability engineering with multi-cloud deployments for enterprise clients.

Resources & benchmarks

Performance snapshot

SuiteBeforeAfterReduction
Full E2E180m42m77%
Core flows45m16m64%
Visual checks30m8m73%
Get pricingPrivacy