Synthetic Users

Product Introduction

1. Synthetic Users by Jina is an autonomous testing platform that simulates real user behavior to perform QA tests, identify hidden bugs, and provide actionable feedback for application improvement.
2. The core value lies in replacing manual testing efforts with AI-driven synthetic users that autonomously validate user flows, monitor production environments, and ensure consistent user experiences without human intervention.

Main Features

1. The platform generates end-to-end test cases from natural language prompts, automatically translating user instructions like "Test the checkout flow" into executable test plans with assertions, steps, and validation criteria.
2. Synthetic users explore applications freely using predefined customer personas (e.g., "Busy Parent Shopper"), uncovering edge-case bugs and UX issues through unscripted interactions that mimic real-world usage patterns.
3. Self-healing tests adapt to UI changes by automatically updating selectors and workflows when elements shift, reducing maintenance overhead compared to traditional brittle test scripts tied to DOM structures.
4. Integration with CI/CD pipelines, APIs, and scheduled runs enables automated testing across deployment stages, with results directly impacting release decisions and A/B test validations.
5. Enterprise-grade security ensures data privacy by running tests within the user’s infrastructure, complying with regulatory requirements, and avoiding external data storage.

Problems Solved

1. Eliminates the need for manual test script creation and maintenance, addressing the time-consuming and error-prone nature of traditional QA processes.
2. Prevents user experience degradation by identifying performance bottlenecks (e.g., dashboard widgets taking 8+ seconds to load) and functional breaks (e.g., broken SMTP configurations) before they impact real users.
3. Enables comprehensive testing for complex scenarios such as multi-step checkout flows, persona-based A/B testing, and production monitoring without requiring dedicated QA resources.

Target User Group

1. Engineering and DevOps teams seeking to automate regression testing while accelerating release cycles.
2. Product managers validating feature usability and A/B test outcomes against specific customer personas.
3. QA teams transitioning from manual testing to AI-assisted workflows for cross-browser and cross-device compatibility checks.

Typical Use Case Scenarios

1. Regression testing after code updates, where synthetic users verify critical paths like user registration, payment processing, and inventory management.
2. Pre-launch validation of new features, such as testing a redesigned checkout flow’s compatibility with saved payment methods and coupon code integrations.
3. Continuous production monitoring with scheduled test runs every 4 hours to detect API failures, slow-loading components, or broken third-party integrations.

Unique Advantages

1. Unlike static testing tools, Synthetic Users employs adaptive AI that learns application structures, enabling self-healing tests that survive UI redesigns and component library updates.
2. Combines exploratory testing (freely navigating apps) with scripted scenarios (prompt-generated test plans), providing hybrid coverage unmatched by purely scripted or record-and-playback tools.
3. Delivers contextual feedback beyond pass/fail results, including UX scoring (e.g., 8.2/10 for checkout flows), verbatim user sentiment analysis, and comparative metrics against previous versions.

Frequently Asked Questions (FAQ)

1. How do self-healing tests handle UI changes? The AI maps UI elements using multiple attributes (IDs, test IDs, visual patterns) and automatically adjusts selectors when changes occur, reducing false positives from cosmetic updates while maintaining test validity.
2. Can synthetic users replicate specific customer personas? Yes, users define personas with demographic, behavioral, and technical parameters (e.g., "New Team Lead" persona testing SaaS onboarding), which guide the AI’s interaction style and priority paths.
3. Where is test data processed and stored? All testing occurs within the user’s environment with no external data transmission; results are optionally stored in the user’s preferred cloud or on-premises storage.
4. How does pricing scale with usage? Costs are based on test run volume with tiered pricing for high-frequency teams, plus a monthly platform fee adjusted for infrastructure complexity and support requirements.
5. What CI/CD platforms are supported? The product provides API endpoints and webhook integrations compatible with GitHub Actions, GitLab CI, Jenkins, and other major pipeline tools.