AI Automation Test Platform

Panto AI uses machine learning and generative models to create resilient tests, triage failures with human-readable summaries, and auto-heal brittle selectors, so your team spends less time on maintenance and more time shipping.

Trusted by brands, across the globe

FASTER TEST CREATION

LOWER MAINTENANCE

SMARTER TRIAGE

CROSS-PLATFORM

CI-AWARE

Why Choose Panto AI For AI Automation Testing

Faster Test Creation

Generate end-to-end and component tests from user flows, user stories, or plain-English prompts.

Lower Maintenance

AI-powered selector suggestions and auto-repair reduce flaky tests and false positives.

Smarter Triage

Natural-language failure summaries and ranked root-cause candidates speed debugging.

Cross-Platform and Cross-Browser

One platform for web, mobile, WebView, and hybrid app automation.

CI-Aware

Integrates into your pipelines to gate merges with confidence.

How AI in Automation Testing Works

AI transforms test automation from a manual, time-intensive process into a fast, intelligent workflow. Panto handles everything from test creation to maintenance and failure analysis using reinforcement learning and generative AI.

Describe or import your test flow

Provide a prompt, user story, or record a flow. Panto understands intent and prepares it for automation.

Generate & refine tests with AI

AI creates executable test scripts with suggested selectors and assertions, which teams can review and adjust.

Run tests across environments

Execute tests across browsers, devices, or apps, with CI integration and parallel execution.

Analyze, auto-heal & improve

Get AI-generated failure insights, auto-healing fixes, and continuous optimization to keep tests stable over time.

Provides a Full Audit Trail

Teams can review what was healed, why, and what changed—full transparency, no black box.

Key Capabilities of AI-Based Test Automation

AI-Powered Test Generation

Automatically create end-to-end tests from prompts, user stories, or recorded flows—no manual scripting required.

Self-Healing Test Automation

Adapt to UI and DOM changes with intelligent selector updates, reducing flaky tests and maintenance overhead.

Semantic & Intent-Based Assertions

Validate user outcomes (not just elements) with context-aware assertions that reflect real user behavior.

Flaky Test Detection & Smart Retries

Identify unstable tests using ML patterns and apply targeted retries instead of blind re-execution.

Natural Language Test Creation

Write and understand tests in plain English, making automation accessible across product, QA, and engineering teams.

Generative AI Failure Analysis

Get human-readable summaries of failures, root cause insights, and suggested fixes to speed up debugging.

Cross-Platform Test Automation

Generate and execute tests across web, mobile, WebViews, and native frameworks from a single flow.

Test Intelligence & Optimization

Continuously analyze test performance, remove redundancies, and improve execution efficiency over time.

Ideal Use Cases

Auto-generate E2E suites for onboarding

Maintain a large test corpus by using auto-heal and selector recommendations

Reduce triage time by auto-generating Slack or Jira tickets

Informed analysis via AI summaries, stack traces, and suggested changes

Auto-generate critical funnel flows from product stories

Create load-lite smoke tests that exercise important user journeys before full load tests

Validating in-app WebView content/deep links across Android OEM browsers

Why Teams Choose Panto's AI Automation Testing Platform

Instead of spending time fixing tests, teams get intelligent automation that creates, adapts, and improves itself with every run. Panto redefines how teams build and maintain automation with AI at the core, eliminating manual scripting, flaky tests, and constant upkeep.

Book a demo to see Panto's AI automation testing in action.

FAQ's

AI test generation works by interpreting a natural language description of a user flow, mapping it to concrete UI interactions on a real device or browser, and converting those interactions into a deterministic script. The script uses stable element identifiers and smart waits rather than brittle XPath selectors, so the test runs reliably without the manual work of writing or debugging automation code. Panto follows this approach to let teams go from feature description to running test in minutes.

Stability comes from separating the intent layer from the execution layer. Panto captures the flow using an AI agent, then converts it into a deterministic script that does not rely on a live model during execution. Combined with semantic element recognition that adapts to minor UI changes and smart waits that respond to actual app state, the result is a test that runs consistently across environments and over time.

When a test step fails because an element's selector has changed, Panto's AI analyses the surrounding DOM or view hierarchy, identifies the element using its semantic role and visual context, and updates the locator automatically. The heal is logged so engineers can review what changed. This eliminates the routine maintenance cycle of manually updating selectors after every UI release.

AI-generated tests can cover the majority of end-to-end user flows and regression cases without manual scripting. For highly specialised scenarios—complex API assertions, performance benchmarks, custom tooling—manually written tests may still add value. The practical strategy is to use AI generation for broad coverage and reserve manual scripting for edge cases that require precise domain knowledge.