Answers about our software testing, AI testing, test automation, and quality engineering services.
Testrig Technologies uses AI to support different parts of the QA workflow, from application exploration and test generation to execution and failure analysis. The goal is not to replace QA engineers with AI, but to use AI where it can remove repetitive work while keeping human judgment at the center of testing.
We look beyond whether an AI model gives an answer. Our testing can cover prompts, model responses, APIs, RAG workflows, application logic, integrations, reliability, and regression scenarios. The exact approach depends on how the AI is being used in the product and what the expected business outcome is.
Yes. We test AI models and the applications built around them. Depending on the use case, this can include response validation, prompt testing, robustness, regression, API behavior, consistency, and application-level functionality.
Yes. If an application uses different models or switches between model providers, we can build testing around those variations. This can include checking model-specific behavior, validating application outcomes, and making sure a model change does not introduce unexpected regressions elsewhere in the system.
Yes—and this is an important part of AI application testing. A model may behave correctly while the surrounding application has problems. We can test the complete flow, including UI, APIs, prompts, retrieval, integrations, business rules, data flow, and actions triggered by the AI response.
Our approach is human-led and AI-assisted. AI can help generate tests, explore applications, analyze failures, and reduce repetitive work. QA engineers still review coverage, validate business behavior, investigate failures, and make the final quality decisions.
We focus on getting useful feedback earlier in the development cycle. This can involve automation, parallel execution, CI/CD integration, risk-based testing, and AI-assisted QA workflows. The objective is not simply to run more tests—it is to help teams find important issues earlier and spend less time on repetitive testing.
We first understand the application, its critical workflows, existing automation, CI/CD setup, integrations, and current pain points. From there, we decide what should be automated, what needs improvement, and where AI can genuinely help. We don't believe every test needs to be made "AI-powered."
Yes. We work with complex applications where teams need reliable regression coverage, scalable automation, CI/CD integration, and support across multiple technologies. The engagement can focus on a specific automation requirement or a broader QA program.
We combine traditional QA with modern automation and AI-focused testing. Along with web and mobile testing, our capabilities include Playwright, Cypress, Selenium, Appium, AI model testing, AI-assisted automation, performance testing, and security testing. This allows us to work across both conventional software and newer AI-driven applications.
Yes. We can implement AI-assisted self-healing capabilities where they make practical sense. These can help an automated test adapt to certain UI changes, such as modified element locators. However, we don't treat self-healing as a magic fix—tests still need human review when the application's actual behavior or business logic changes.
It can. When the underlying functionality remains the same but UI elements or locators change, self-healing can reduce some of the manual effort involved in updating tests. It does not eliminate maintenance completely, particularly when workflows, business rules, or expected behavior change.
Yes. We work with Playwright and Cypress, as well as Selenium and Appium where they fit the project. The framework should follow the application and testing requirements—not the other way around. We consider factors such as application architecture, browser coverage, CI/CD, team skills, and long-term maintenance before choosing or recommending a framework.
Yes. We can combine AI-assisted testing with frameworks such as Playwright or Cypress to automate web application workflows. Depending on the application, this can include functional testing, regression testing, API validation, cross-browser testing, and CI/CD execution.
Yes. We look at the underlying reason for maintenance problems rather than simply fixing failed scripts one by one. Depending on the project, improvements may include better framework architecture, reusable components, resilient locators, test-data strategies, parallel execution, failure analysis, and AI-assisted maintenance.
Yes. You don't necessarily need to throw away an existing framework to improve your automation. We can review the current setup, identify coverage and maintenance issues, improve the existing framework, or recommend migration when the current approach is creating long-term problems.
Yes. We can integrate automated testing with reporting and dashboard solutions that give teams visibility into execution, failures, trends, coverage, and release quality. The exact dashboard setup depends on the client's existing tools and CI/CD environment.
We work with web applications, mobile applications, APIs, SaaS products, and enterprise applications. Automation is planned around the application's architecture, critical user journeys, integrations, browsers or devices, and release process.
TR-Forge is Testrig Technologies' AI-powered test case generation engine. What makes the approach different is that it does not rely only on a requirement or ticket in isolation. TR-Forge can explore the real application, understand its pages and navigation, and use that application context when generating test cases.
A requirement can tell you what a feature is supposed to do, but it doesn't always tell you how the application actually works. TR-Forge connects those two pieces. It explores the application, builds an understanding of its real workflows, and uses that context along with requirements from sources such as JIRA, Linear, ClickUp, or manual input.
Yes. For complex GenAI systems, testing can cover more than the model itself. We can look at prompts, responses, APIs, RAG pipelines, UI workflows, integrations, business logic, regression scenarios, and the way the application behaves when the model output changes.
AI outputs are not always deterministic, so traditional pass/fail testing isn't enough for every scenario. Depending on the application, we can use evaluation criteria, test datasets, response checks, regression testing, monitoring, and human review for cases where contextual judgment is required. The validation criteria are defined around the actual use case and expected outcome.
Yes. We can test applications where multiple models are used, whether they are from different providers or serve different functions within the same application. Testing can cover model switching, model-specific behavior, regression, response quality, and downstream application behavior.
Yes. We can review the existing automation, application architecture, test coverage, flaky tests, CI/CD setup, and current QA challenges before recommending an approach. This helps identify what should be improved first instead of immediately adding more automation on top of an unstable foundation.
The right comparison depends on what you need from a QA partner. Testrig Technologies combines manual testing, automation, AI-assisted testing, AI model testing, performance testing, and security testing. For automation, we work with technologies such as Playwright, Cypress, Selenium, and Appium, while the specific approach depends on the application and project requirements.
You can reach out to Testrig Technologies through website to discuss your application even start with No-cost 2 weeks PoC. A discussion can focus on the current testing process, areas where automation could help, and whether AI-assisted testing is a practical fit for your application.
Yes. We can assess your current web application and recommend an automation approach based on its technology stack, workflows, existing tests, and release process. Where it makes sense, AI-assisted capabilities can be added to areas such as test generation, maintenance, failure analysis, and regression testing.
