Software Testing and QA Services Company| inVerita

Q: What is the difference between QA and software testing?

Software testing is a subset of QA focused on finding and reporting defects. QA is broader: it encompasses the processes, standards, and practices that prevent defects from occurring in the first place, covering requirements analysis, test planning, process audits, and continuous improvement. Testing is reactive (find the bug); QA is proactive (design the process so fewer bugs are introduced). inVerita operates both: a structured QA practice that informs how development work is done, with testing execution embedded at every stage from requirements through to release.

Q: When should I use manual testing and when is automation better?

Manual testing is most valuable for exploratory testing, usability evaluation, ad hoc testing of new or rapidly changing features, and scenarios requiring human judgment about visual or experiential quality. Automated testing excels at regression testing, performance testing under load, API testing, and repetitive test cases across large datasets. Automation pays for itself when a test runs frequently, across multiple browsers or devices, or in CI/CD pipelines. Most mature QA practices use both: automation for high-frequency stable tests, manual testers for exploration and new feature validation.

Q: How much does QA outsourcing cost?

QA consultant for process assessment typically costs $75 to $150 per hour. A dedicated QA engineer embedded in your team costs $3,000 to $7,000 per month depending on seniority and specialization. A full QA engagement covering test strategy, automation framework setup, manual testing, and reporting typically runs $15,000 to $80,000 depending on scope and duration. QA typically represents 20 to 30% of total software development budget. IBM research shows that fixing a bug found in production costs 100 times more than fixing the same bug during development.

Q: Can AI write test cases automatically for my application?

AI can generate meaningful test cases automatically for well-structured APIs, unit-testable business logic, and components with clear input/output contracts. Tools like Diffblue Cover analyze Java code and generate JUnit tests autonomously. GitHub Copilot generates test cases from function signatures and docstrings. For complex UI workflows or edge cases in legacy systems, AI-generated tests require human review and refinement. In practice, AI handles the first 50 to 70% of test creation (standard paths, boundary conditions, error handling), and QA engineers focus on complex scenarios, performance characteristics, and security edge cases that require domain knowledge.

Quality Assurance and Software Testing Services at inVerita

inVerita QA teams have vast experience in continuous testing practices, creating both test solutions from scratch and improving the existing ones by leveraging the best tools and practices on the market with customizations requested by our Customers.

HOW WE CAN HELP

Building Testing Process from a Scratch

Analyzing the System

Developing Proof of Concept

Creating a Unified Testing Approach

Defining Test Scope and Clearing Data for Testing

Representing Test Results

Enabling the Continuous Integration

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Optimizing Existing Testing Processes

Assessing the Existing Processes to Identify Areas for Improvement

Estimating the Roadmap and Choosing Tech Stack

Providing Solutions Based on the Best Testing Practices

Improving Test Coverage and Scope

Developing Complex Documentation

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Our QA and Software Testing Services

Testing Consulting

Our consultants bring a fresh perspective and deep knowledge of how to improve the processes in your team to achieve maximum efficiency. With QA consulting input, you will be able to create a flawless strategy to upgrade your testing operations and achieve even the most ambitious results in record time.

Testing Outsourcing

inVerita testing teams take over all of your QA needs and give you a product of flawless quality, unwavering reliability, and engaging UX. We offer our support for both long-term cooperation and short-time assistance with a set and clear budget.

Manual Testing

inVerita QA engineers provide comprehensive manual testing services to minimize the risks associated with code errors or abnormal application behavior. By identifying bugs early in the development process, we help teams prevent issues from escalating and ensure a productive, efficient development cycle from start to finish.

Automation Testing

Automated testing is fast, cost-effective and precise which is especially beneficial for testing large projects and software built over multiple iterations. Our QA teams implement and improve test automation processes that abide by the best practices, with customizations requested by the customer to optimize the development workflow, significantly reducing Time-to-Market.

Types of Software Testing And QA Services at inVerita

We execute various types of software testing not only to identify bugs and fix them, but rather to monitor performance, reliability, scalability, usability, easy access, UI, functionality, and behavior.

Usability Testing

Usability testing is required to ensure the building of an effective, efficient, and enjoyable experience for users through testing the functionality on real people.

Functional Testing

A kind of black box testing is performed to confirm that each function gives the value, determines the output, and verifies the actual output with the expected value.

Performance Testing

We evaluate the speed, responsiveness, and stability of the software under the workload to guarantee that your product can handle high data loads and user spikes.

Change-Related Testing

Change-related testing aims to ensure that the previously eradicated bugs have been fixed and new builds or code changes do not adversely affect the system.

Accessibility Testing

We evaluate a system's ability to be usable by people with disabilities, ensuring compliance with accessibility standards and guidelines. This includes testing for screen reader compatibility, keyboard navigation, and alternative input methods to make sure everyone can access and use the application effectively.

Compatibility Testing

Testing the software in different environments to ensure that it is good enough to run in different browsers, operating systems, databases, hardware, devices, and networks.

Localization Testing

We check the appropriate linguistic and cultural aspects of the software for a particular locale, including cultural appropriateness of UI, typographical errors, etc.

Acceptance Testing

The last phase of software testing is to evaluate the system’s compliance with the business requirements and assess its readiness for delivery.

CASE STUDY

Telemedicine Platform

We executed testing in parallel with development at both the UI and API levels. With the help of our DevOps professionals, our test automation team designed and implemented a continuous integration pipeline and introduced continuous testing into it. This way, we managed to verify new software parts faster and more efficiently while not compromising on software quality. As a result, we managed to reduce time-to-market by 40%.

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“They have exceptional company culture and provide excellent code quality, always delivering on time”

CEO, Healthcare Company

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Software testing services and QA services company

Industries We Serve

Custom Healthcare Software Development Services

We engineer scalable health information exchange systems, cloud-based telehealth software, patient engagement solutions, EHR & EMR software, and support any kind of clinical software development specifically tailored to the requirements of your organization.

Fintech Software Development Services

With more than 6 years of experience in banking and financial services software, our dedicated teams have developed Fintech solutions in digital banking, payment and investment management, crypto, trading, and hedge fund spheres.

Custom Logistics Software Development

inVerita is a logistics software development company that delivers custom software solutions to help enterprises optimize inventory management, streamline warehouse operations, and reduce delivery times.

Personalized Software Development Services For Retail That Suit Your Business Needs

We provide consulting and engineering services for building powerful e-commerce platforms to establish meaningful relationships with your customers and increase the company’s profitability.

Real Estate

We help real estate providers, commercial properties managers, and owners build facility-specific solutions to optimize business processes and result in better operational efficiency.

E-learning

Startups, educational institutions, and enterprises build with us next-generation software leveraging technologies in web, mobile, cloud, IoT, product design, and analytics to make the learning process globally accessible, interactive, and affordable.

Benefits of Quality Assurance and Software Testing Services at inVerita

Vast Experience

inVerita is a QA services company with experience in working with business cases of different shapes and sizes and from various industries. Quality assurance and testing services are an integral part of 90% of our long-term projects.

End-to-End Testing Processes

We cover all software testing stages from requirements analysis and test planning, test execution and defect reporting to release testing and maintenance. Our specialists ensure that all testing activities smoothly integrate with your development process.

Full Transparency

Throughout the entire software testing process, you will have absolutely 100% of control and transparency. From test results to test scope, we will make sure that you have a detailed status report every time a scan of the project is performed.

Frequently Asked Questions

What is the difference between QA and software testing?

Software testing is a subset of QA focused specifically on finding and reporting defects in the software. QA (Quality Assurance) is broader: it encompasses the processes, standards, and practices that prevent defects from occurring in the first place. QA covers requirements analysis, test planning, process audits, documentation standards, and continuous improvement of the development workflow. Testing is reactive (find the bug); QA is proactive (design the process so fewer bugs are introduced). inVerita operates both: a structured QA practice that informs how development work is done, with testing execution embedded at every stage from requirements through to release.

When should I use manual testing and when is automation better?

Manual testing is most valuable for exploratory testing (finding unexpected behaviors by thinking like a user), usability testing, ad hoc testing of new or rapidly changing features, and scenarios that require human judgment about visual or experiential quality. Automated testing excels at regression testing (verifying that existing functionality still works after changes), performance testing under load, API testing, and repetitive test cases across large datasets. Automation pays for itself when a test needs to run frequently, on multiple browsers or devices, or as part of a CI/CD pipeline where speed matters. Most mature QA practices use both, with automation covering the high-frequency stable tests and manual testers focusing on exploration and new feature validation.

What testing tools and frameworks does inVerita use?

inVerita's automation stack is built around Selenium and Playwright for web UI testing, Appium for mobile automation (iOS and Android), JMeter for performance and load testing, and Postman and RestAssured for API testing. For CI/CD integration, tests run in pipelines built on Jenkins, GitHub Actions, and Azure DevOps. Test management is handled via Jira with Zephyr or TestRail for structured test case management and reporting. Tool selection for each project is driven by the technology stack, application type, and team familiarity to minimize ramp-up time and maximize framework longevity.

How is QA integrated into CI/CD pipelines?

In a mature CI/CD pipeline, every code commit triggers an automated test run: unit tests run in seconds, integration tests in minutes, and regression suites in the background, with results surfacing in the pull request before code is merged. This shift-left approach catches defects at the point of introduction rather than at the end of a sprint or release cycle. inVerita demonstrated this on a telemedicine platform project: by designing a continuous testing pipeline integrated with CI, time-to-market was reduced by 40% while maintaining quality standards. The pipeline runs at both UI and API levels, giving full coverage without the bottleneck of end-of-sprint manual regression.

How much does QA outsourcing cost?

QA outsourcing costs vary by engagement model. A QA consultant for process assessment and strategy design typically costs $75 to $150 per hour. A dedicated QA engineer embedded in your development team costs $3,000 to $7,000 per month depending on seniority and specialization (manual vs. automation). A full QA engagement covering test strategy, automation framework setup, manual testing execution, and reporting typically runs $15,000 to $80,000 depending on project scope and duration. QA typically represents 20 to 30% of total software development budget, and IBM research consistently shows that fixing a bug found in production costs 100 times more than fixing the same bug during development.

How is AI changing software testing in 2026?

AI is transforming testing in three ways. AI test generation tools such as GitHub Copilot, Diffblue, and Testim analyze codebases and generate unit and integration test cases at 3 to 5 times the speed of manual test writing, dramatically expanding coverage for legacy systems with low test coverage. AI-powered test maintenance automatically updates test scripts when UI changes occur, eliminating the most labor-intensive part of sustaining an automation suite. Visual AI testing tools detect unintended UI changes at a pixel level across browsers and screen sizes. The net effect is that test coverage increases while the time QA engineers spend on mechanical test creation decreases, freeing them for exploratory testing and test strategy work.

Can AI write test cases automatically for my application?

AI can generate meaningful test cases automatically, particularly for well-structured APIs, unit-testable business logic, and components with clear input/output contracts. Tools like Diffblue Cover analyze Java code and generate JUnit tests autonomously. GitHub Copilot generates test cases from function signatures and docstrings. For complex UI workflows, custom business logic, or edge cases embedded in legacy systems with no documentation, AI-generated tests require human review and refinement to be reliable. The practical outcome is that AI handles the first 50 to 70% of test creation (standard paths, boundary conditions, error handling), and QA engineers focus effort on the complex scenarios, performance characteristics, and security edge cases that require domain knowledge.