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Stores and queries continuous application profiles to reveal resource use down to code-level details.

Model continuous-delivery pipelines and trace changes from source to deployment.

Continuously fuzz accepted open-source projects to find crashes and other input-handling bugs.

Define pull-request, build, and deployment jobs in screwdriver.yaml with pluggable execution engines.

Collect continuous CPU and memory profiles, then compare code paths over time.

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

Use continuous and categorical colour maps in Python visualizations.

Connect product feedback with customer context and repeatable analysis.

Continuously runs .NET tests in Visual Studio or Rider and reports results as code changes.

Read customer feedback alongside the accounts and business issues behind it.

360-degree feedback projects with questionnaires and downloadable individual reports

Experience feedback records with delivery paths and tracked follow-up

Customer and employee surveys connected to feedback analysis and follow-up

Performance reviews with related feedback and manager context

Performance cycles with review templates, feedback, and calibration

Analyze customer feedback with shared filters, themes and original quotes.

Customer feedback collection connected with follow-up workflows

A shared repository for interviews and customer feedback

Goal-linked feedback from colleagues and customers

AI-powered customer feedback platform for surveys, forms, and research.

Analyze themes and sentiment across connected customer-feedback sources.

Survey collection for customer, employee, and event feedback

Survey responses that trigger notifications, classification, and follow-up tasks

Session-linked event surveys that stay open for later feedback