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Regression Guard that compares release candidates with production behaviour

Creates API and integration regression tests from API specifications or recorded application traffic.

A managed identity-monitoring and assistance service

Automate unit and regression testing for embedded C/C++ software, from test design to target execution and coverage analysis.

Turns API definitions and collections into mocks, then runs contract-conformance and non-regression tests.

Compare application screenshots with approved visual baselines in automated UI tests.

Compare synthetic browser checks with real-user performance and alert on regressions.

LLM tracing and quality monitors built around OpenTelemetry instrumentation.

Create visual, no-code browser tests with AI planning and API or CLI runs for CI.

SEO automation that finds keywords, writes articles, and builds backlinks

SEO and GEO content platform that audits, writes, publishes and tracks pages for Google and AI search.

Runtime FinOps that meters, budgets, routes and charges back every model call.

Browser-based statistics from pasted data

Record and schedule browser tests for web flows without writing the test steps by hand.

Group mobile app crashes, non-fatal errors, and Android ANRs into issues that teams can prioritize by user impact.

Run visual, interaction, and accessibility checks on frontend UI states in real browsers.

A command-line tool for sending HTTP requests and asserting the responses as repeatable tests.

A visual testing service that compares screenshots and other snapshots from CI builds against a selected baseline.

Records web and mobile user flows as hosted automated tests, with AI prompts and run diagnostics.

Find production performance bottlenecks in PHP and Python applications, then inspect the responsible code paths.

Compare font-family revisions through rendered words and font data.

A visual and code-based automation tool for functional web and desktop testing, with API and load features in the Ultimate edition.

Fit, evaluate, and reuse Python models for common supervised and unsupervised machine-learning tasks.

Organize test cases, plans, and execution results so a team can track quality across releases.