
Sleuth is an automated deployment tracking and analysis tool for engineering teams - Gain visibility and control over your releases and reduce deployment risk.
Product Description:
Sleuth is a DevOps analytics platform that helps teams to accelerate their development cycles by providing comprehensive insights into the software delivery process. The platform is designed to help engineering and DevOps teams gain visibility into their deployments, track the performance of their systems, and identify issues early on. With Sleuth, teams can improve their delivery speed, reduce downtime, and increase the overall reliability of their software.
Sleuth's analytics engine tracks all changes and deployments across a wide range of tools and systems, including Git, Jira, CircleCI, and Kubernetes. This allows teams to see how their deployments are progressing and quickly identify any issues that may arise. The platform also provides alerts and notifications for issues such as slow builds, failing tests, or errors in production. This enables teams to take proactive measures to resolve issues before they become critical.
Sleuth offers a range of powerful analytics features, including cycle time analysis, lead time analysis, and deployment frequency analysis. These features help teams to identify bottlenecks and areas for improvement in their software delivery processes. Additionally, Sleuth provides customizable dashboards and reports that allow teams to track and visualize their metrics in real-time.
Sleuth is built with security in mind, and all data is encrypted at rest and in transit. The platform integrates seamlessly with a wide range of tools, including Slack, PagerDuty, and Datadog. This makes it easy for teams to incorporate Sleuth into their existing workflows and toolchains.
In summary, Sleuth is an essential tool for any engineering or DevOps team looking to accelerate their software delivery processes, gain visibility into their deployments, and improve the reliability of their systems.



Supported platform:
WINDOWS, MAC
Language:
English
Average Team Size:
200