AI benchmarking & evaluation
Compare model and prompt versions, evaluate retrieval and responses, inspect agent traces, and track experiments.

Playwright, Selenium, and JMeter are part of our core toolkit. We work with your existing QA stack and select tools around your application, team, and release process.
A broad selection of common tools and platforms across software testing. The project scope determines the stack, integrations, licenses, and test coverage.
Compare model and prompt versions, evaluate retrieval and responses, inspect agent traces, and track experiments.
Build repeatable browser journeys, regression suites, and cross-browser checks around critical product behavior.
Exercise native and hybrid applications, platform-specific interactions, and mobile regression scenarios.
Check payloads, authentication, business rules, service contracts, and dependency behavior with controlled mocks.
Model realistic traffic, increase demand, run sustained workloads, and measure response times and errors.
Organize assertions, fixtures, test data, and behavior-driven scenarios in maintainable test suites.
Run selected checks on pull requests, schedule deeper regression, and publish reports as build artifacts.
Prepare repeatable test execution environments and support testing across container and cloud deployments.
Expand the agreed browser, operating system, and real-device matrix without maintaining every device locally.
Correlate test results with available metrics, logs, and traces to investigate application bottlenecks.
Combine automated accessibility checks with keyboard and screen-reader review; compare visual changes against approved baselines.
Support scoped application security checks and static analysis alongside functional testing and manual investigation.
Connect requirements, test cases, execution evidence, and defects so release decisions remain traceable.
Choose the language and framework your team can maintain. Use scripting and SQL checks to support test data setup, API validation, and repeatable investigations.
Browser and device coverage, API protocols, delivery cadence, reporting needs, and the skills of the team maintaining the suite shape the tooling decision. We define the checks and expected evidence before expanding the stack.
Use API or component checks where they provide direct feedback. Reserve end-to-end journeys for workflows that need the full application.
Include request details, traces, screenshots, and run metadata appropriate to the test. Make results accessible to the people fixing the issue.
Version test code and configuration, isolate test data, and document dependencies and environment assumptions.
Agree who owns the suite, how flaky tests are investigated, and which checks belong in each delivery stage.