middleware

It surfaces engineering metrics aligned with the DORA (DevOps Research and Assessment) frameworkdeployment frequency, lead time for changes, change failure rate, and mean time to recoveryhelping engineering managers and team leads understand where delivery bottlenecks and quality risks exist without requiring manual data collection.

The platform integrates with GitHub, GitLab, Jira, PagerDuty, and other tools in the developer toolchain to build a unified picture of the software delivery process.

AI analysis surfaces patterns that raw metrics miss: identifying which types of changes have higher failure rates, which team members are review bottlenecks, when PR cycle times spike and why, and which services have incident rates inconsistent with their deployment pace.

This contextual analysis turns raw engineering data into prioritized improvement recommendations.

Engineering directors, VPs of engineering, and team leads at technology companies use Middleware to make data-driven decisions about engineering process improvements without requiring engineers to manually instrument their workflows or fill out weekly surveys.

The DORA metrics framing aligns the platform with widely-adopted engineering effectiveness benchmarks, making it straightforward to compare team performance against industry baselines and communicate engineering health to non-technical stakeholders.

Its automated data collection model means insights are generated from existing toolchain activity rather than requiring teams to change their working practices to generate the data.