Pie

Product Introduction

1. Pie is an AI-powered quality assurance platform that automates end-to-end testing for web and mobile applications by simulating real user interactions. It deploys AI agents to execute comprehensive test scenarios, achieving 80% test coverage within 30 minutes without requiring manual scripting or prior setup. The platform integrates directly into existing development workflows to provide actionable insights for release decisions.
2. The core value of Pie lies in eliminating the trade-off between rapid deployment and software reliability by automating QA processes with AI-driven precision. It enables engineering teams to identify critical issues before production releases while reducing testing time from thousands of hours to minutes. This ensures continuous delivery of high-quality software without compromising velocity.

Main Features

1. Pie deploys AI agents that autonomously generate and execute test cases by mimicking real user behavior, achieving 80% end-to-end test coverage within 30 minutes for web, mobile, and React Native applications. Tests adapt dynamically to UI changes, minimizing maintenance overhead.
2. Users can define custom test scenarios in natural language, such as "test checkout with expired credit card" or "verify admin can’t access user data," which Pie translates into executable workflows. This combines domain expertise with AI to validate complex user journeys.
3. The platform provides a single readiness score that consolidates test results, security checks, and compliance metrics into a go/no-go release decision. This score is derived from detailed reports on coverage gaps, UI inconsistencies, and security vulnerabilities.

Problems Solved

1. Pie addresses the challenge of maintaining software quality in fast-paced development cycles, where manual QA processes create bottlenecks and incomplete test coverage leads to production failures.
2. The platform targets engineering teams, product managers, and DevOps professionals in organizations prioritizing continuous delivery but struggling with QA scalability.
3. Typical use cases include validating critical user flows (e.g., e-commerce checkouts), ensuring compliance with security protocols, and testing cross-platform compatibility for apps built with frameworks like React Native.

Unique Advantages

1. Unlike script-based testing tools, Pie requires no code or predefined scripts, using AI to autonomously generate and maintain tests based on real user interaction patterns.
2. The platform is framework-agnostic, testing applications at the UI layer to ensure compatibility with any tech stack, including legacy systems and modern frameworks like React.
3. Pie holds SOC 2 Type 2 certification and operates with zero source code access, ensuring enterprise-grade security while integrating seamlessly into CI/CD pipelines, version control systems, and incident management tools like Jira.

Frequently Asked Questions (FAQ)

1. How do I get started with Pie's AI-powered QA testing? Users can initiate testing by uploading their application’s URL or binary, followed by defining test objectives via natural language prompts or predefined templates. A live demo session is available to map Pie’s capabilities to specific use cases.
2. What do I need to run my first automated test with Pie? No prior setup or scripting is required—users only need to provide application access (URL, APK, or IPA) and specify test scenarios in plain English. Pie’s AI handles test case generation, execution, and result analysis.
3. How quickly does Pie deliver test results and coverage? The platform generates initial test results and 80% coverage metrics within 30 minutes of deployment. Continuous testing modes provide real-time feedback during development sprints.
4. How does Pie handle UI changes and test maintenance? Pie’s AI agents automatically detect UI element changes and adjust test scripts dynamically, reducing maintenance effort by 90% compared to static testing frameworks.
5. Do I need to write test scripts or code with Pie? No coding is required—tests are created through natural language inputs or automated exploration. Pie converts these inputs into executable workflows using adaptive machine learning models.