Sii offers comprehensive Testing & QA services built around an AI-native quality workflow, helping you accelerate application development, reduce the risk of defects, and improve software quality.
Sii automates UI, API, and regression testing using an AI-supported approach to reduce manual testing, accelerate releases, and lower the risk of defects with every change:
Sii evaluates system performance before a launch, marketing campaign, or expected increase in user traffic:
Sii tests AI systems across the entire lifecycle – from data quality to the AI platform itself:
Sii provides end-to-end computerized system validation (CSV) in line with GxP requirements and industry standards:
Sii assesses and optimizes QA processes to reduce testing costs and minimize defects:
Sii selects, implements, and integrates leading enterprise-grade testing platforms:
Sii tests embedded software for critical systems in accordance with industry standards:
Sii simulates real-world attacks and audits security controls to help ensure robust protection:
Sii expertise and AI combined throughout the testing lifecycle
We get to know your product, business objectives, and potential risks. AI supports document analysis and helps identify areas that require particular attention.
We design a testing process tailored to the project and its business priorities. AI helps recommend test scenarios and assess requirements coverage.
We create manual and automated tests and integrate them into the software development process. AI accelerates the preparation of test cases, test data, and automation code.
We execute tests and analyze the results to quickly detect and eliminate defects. AI supports log analysis, defect classification, and the identification of likely root causes.
We analyze the effectiveness of the QA process and identify opportunities for improvement. AI helps detect trends, risk areas, and further automation potential.
We get to know your product, business objectives, and potential risks. AI supports document analysis and helps identify areas that require particular attention.
We design a testing process tailored to the project and its business priorities. AI helps recommend test scenarios and assess requirements coverage.
We create manual and automated tests and integrate them into the software development process. AI accelerates the preparation of test cases, test data, and automation code.
We execute tests and analyze the results to quickly detect and eliminate defects. AI supports log analysis, defect classification, and the identification of likely root causes.
We analyze the effectiveness of the QA process and identify opportunities for improvement. AI helps detect trends, risk areas, and further automation potential.
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More than 1,500 QA experts and test engineers support projects for clients across banking, life sciences, automotive, and retail. As a Tricentis Gold Partner and mabl partner, Sii combines deep industry expertise with modern software testing tools, helping organizations improve application quality, accelerate the delivery of changes, and reduce business risk.
See how Sii experts used Tricentis Tosca to deliver an AI project for a healthcare client.
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Frequently asked questions about Testing & QA
The cost depends on the engagement model, testing scope, and team availability requirements. Project-based outsourcing is typically billed on a fixed-price basis for a defined scope or under a time-and-materials model, while a dedicated team is usually charged at a fixed monthly rate.
The starting point for an estimate is a scope analysis covering factors such as the number of applications, level of automation, regulatory requirements, and expected release frequency.
With staff augmentation, the client manages the testers’ work and remains responsible for the methodology and process.
With QA outsourcing, responsibility for the testing process, selection of methods, execution, and reporting shifts to Sii. The client receives outcomes and quality metrics rather than additional resources to manage.
The transition starts with an audit of the existing process and a transfer of product knowledge, followed by a period of parallel work with the current team. Only after this stage does Sii assume full responsibility.
The duration depends on system complexity and the state of the documentation, but it is typically measured in weeks rather than months.
Sii is responsible for the testing process, its execution in accordance with the agreed strategy, and accurate reporting on quality status.
The release decision remains with the client and is made based on the metrics provided. The division of responsibilities, scope, and service levels are defined in the contract.
Yes. Sii can act as an independent party verifying software quality before acceptance, performing acceptance testing and validating acceptance criteria.
This model was used, among others, in the Quality Control Center for ABB, which covered systems delivered by third-party vendors.
Yes. The team works within the client’s existing tools and processes, including Jira, ALM, selected automation platforms, and CI/CD pipelines.
If opportunities to improve the tools or process are identified during the engagement, recommendations are presented separately as part of the optimization work.
The first working regression tests are typically available within a few weeks of project start, while achieving full coverage of key business paths is usually a process measured in months.
The timeline depends on the number of applications, interface stability, availability of test environments, and access to test data. Sii starts with the framework architecture and the most resource-intensive areas of regression testing, so measurable benefits can be achieved before the entire implementation is complete.
Selenium and Playwright work well when the team has strong development skills and wants full control over the test code.
Tricentis Tosca and mabl are enterprise-grade platforms that reduce the amount of coding required and scale more effectively across organizations with multiple systems and teams with varying skill sets.
Sii selects tools based on the client’s infrastructure, budget, and team capabilities. In some cases, the recommendation may be to continue using the existing solution.
The starting point is the cost of the current regression process: the time required to execute it multiplied by the number of releases per year and the cost of the team involved.
Benefits include shorter release cycles, earlier defect detection, and a reduced risk of production failures.
In a project for a global booking platform, automation reduced regression testing from 3 weeks to 2 days and cut the number of test cases from 36,000 to 9,000.
Automated tests are integrated into existing pipelines so they can run with every build or at defined quality gates.
The test scope is selected based on risk and the extent of the changes, enabling the team to receive feedback within minutes rather than after an overnight test run.
Results are reported in the tools already used by the team, without requiring changes to the software development process.
Governance includes a clear distinction between decisions that can be made automatically and those that require human approval, logging of agent activity, controls over data access, and rules for handling sensitive information.
It is also important to define which test artifacts may be generated by AI and which require an engineer’s authorship – particularly in regulated environments.
The most common sources of savings include eliminating redundant test cases, selecting regression scope based on risk rather than running the entire suite, and moving some tests from the UI layer to the API layer.
In one project, optimizing the test suite reduced the number of test cases from 36,000 to 9,000 while shortening regression testing from 3 weeks to 2 days.
Self-healing mechanisms automatically adapt the way user interface elements are identified when minor changes occur, preventing tests from failing every time the application’s appearance is modified.
This reduces the maintenance effort associated with UI tests, which are particularly sensitive to interface changes.
However, self-healing does not eliminate the need for test reviews, as an interface change may also indicate an actual change in functionality.
Compliance means that both the testing scope and the way tests are planned, executed, and documented meet the requirements of the applicable industry standard.
In practice, this requires traceability between requirements, test cases, and evidence of execution, as well as documented acceptance criteria.
Sii guides clients through the entire process – from defining objectives to preparing formal documentation.
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