Neuro is an engineering and quality management platform that integrates data from various tools like Jira and Jenkins to provide real-time visibility into engineering performance and quality metrics. It enables teams to track progress, identify roadblocks, and align their efforts with business priorities, ultimately enhancing operational efficiency and decision-making.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams often struggle with fragmented data across various tools like Jira and Jenkins, leading to a lack of real-time visibility into engineering performance and quality metrics. This makes it difficult to track progress, identify bottlenecks, manage dependencies, and align engineering efforts with business priorities. Consequently, decision-making is hampered by delayed or inaccurate reporting.
Solution
Neuro is an engineering and quality management platform that aggregates data from existing toolchains to provide comprehensive visibility into software development performance. The platform enables teams to track progress against user-defined goals, identify roadblocks, and manage dependencies across teams, including third parties. Smart dashboards automate reporting and provide insights into key metrics, helping teams align their efforts with business priorities and improve operational efficiency. By providing real-time data and analytics, Neuro facilitates data-driven decision-making and helps organizations eliminate waste and realize value.
Target Audience
Neuro is designed for engineering, quality, and product leaders, as well as CXOs, who need real-time visibility into engineering performance and quality metrics to improve decision-making and align efforts with business priorities.
Features
- Out-of-the-box integrations with tools like Jira, Jenkins, and Zephyr for automated data normalization across multiple nodes.
- Customizable dashboards for real-time reporting on engineering and quality performance.
- User-defined goals to track progress and identify roadblocks.
- Dependency management across teams, including third parties.
- Automated reporting to reduce administrative overhead.
- Machine learning to assess commit risk based on developer experience, lines added, and files affected.
- Real-time identification of risky commits to inform testing.