Measure the actual impact
of AI on your Software Development
Get tangible & factual metrics to assess the actual effectiveness & ROI of AI on your Software Development.
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Join 6,000+ software teams in the Engineering Analytics revolution
From startups to large enterprises, Keypup serves all the unique complexities related to project size, structure and teams, including:
“Keypup is a highly useful and practical platform, boasting user-friendly features and lightning-fast report generation.
The service provided by customer support was excellent, showcasing their dedication to customer satisfaction. We are delighted to be part of the Keypup community.”
“Keypup has been instrumental in helping us gain a better perspective on our engineering activities and identifying bottlenecks. Its ease of use combined with its comprehensive features made a difference for us”
“Great product with great support!
Keypup is extremely flexible in its reporting. Once you get your raw data connected, there is almost nothing it can't do. There is a wealth of tables, charts and other reports available. As Director of a software development team, I use Keypup to report on our work efficiencies to senior managment. Keypup makes this task very simple to produce each week.”
Finally Assess Quantitatively & Measure
The Actual & Real Benefits of AI for your Development
Evaluate the overall team's performance with DORA
DORA metrics evaluate deployment frequency, lead time for changes, mean time to restore, change failure rate, and reliability. These metrics provide visibility into a team's agility, operational efficiency, and velocity, serving as proxies for how well an engineering organization balances speed, quality, and security.
This set of metrics allows you to establish a before AI / After AI picture of your overall team's efficiency and work quality. Effective AI implementation should lead to overall metrics improvements.
Analyse your Lifecycle efficiency at Product level
The Issue Cycle Time dashboard provides a detailed analysis of the entire development process, from backlog to release. By tracking the time spent in each stage (pickup, implementation, QA, and release), you can identify bottlenecks that slow down development and ultimately impact delivery speed. This is a detailed analysis at product level.
Effective AI implementation should lead to an overall reduction of time spent in each phase, or at least in the phases where AI is assisting development.
This data-driven approach allows for targeted improvements to optimize workflow, enhance estimations, and measure team performance. The insights gained help companies streamline processes, minimize delays, and maintain an agile delivery flow, ultimately leading to faster and more efficient software delivery.
Analyse your Lifecycle efficiency at Code level
The Pull Request Cycle Time dashboard is a zoomed-in view of the previous one. It allows for analyzing all stages of the Software Development phase (Coding Time, Idle Time, Review Time, Merge Time), and helps identifying bottlenecks in the development.
Effective use of AI-assisted development (for instance use of GitHub Copilot) should lead to a decrease of times spent in stages and an accelerated overall Development Process.
Verify your Developer's productivity is rising
The Developer Summary Dashboard offers a detailed overview of engineering metrics for individual users or teams, providing valuable insights into delivery performances and potential roadblocks.
The Developer Summary Dashboard brings key engineering metrics into focus (such as Commit Frequency, Deployment Frequency, Average PR size, Average Review Duration), enabling teams to contextualize their delivery performances and pinpoint delivery issues.
Effective implementation of AI should lead to increased individual performance, resulting in less overworked team members, or more availability to tackle new tasks.
Assess where Engineering Value is now Allocated
The Value Stream Management dashboard provides a comprehensive overview for strategic Software Development management. It is used to monitor and enhance the value delivered by your engineering teams, featuring key metrics such as Project Effort Distribution, Workload Categorization, Engineering Proficiency, Workload Distribution, Work Pattern Analysis, etc.
Effective, targeted and methodical use of AI-assisted development should lead to more time spent on high-value activities to maximize business value & teams' motivation.
Software Development Analytics to Unlock Your Engineering Teams' Potential
How can you improve what you can't effectively measure?
Keypup's software development analytics layer puts engineering metrics at the forefront. It helps explore, interpret, and configure real-time development data into fully customizable metrics — reports, charts, cards, KPIs, and dashboards — to enable immediate contextualization, better governance, and scale across the organization.
Go Beyond Git Analytics
Connect and Explore
Connect to your git repo(s) and ticketing/project management platform(s) and transform your software development meta-data silos into a unified, detailed, and solution-centric ecosystem with Keypup's Canonical Data Model (CDM).
Leverage Keypup’s engineering metrics builder to extract decision-enabling visualizations, highlight roadblocks, and power efficiency.
Visualize and Analyze
Choose from best-in-class templates, or build live, easy-to-understand visuals — pick the best way to extract raw data and expose meaningful narratives to your organization.
Use our assisted or advanced metrics builder to leverage and adapt pre-built dashboards and in-house metrics. Create and collaborate on personalized and documented views to uncover decisive information.
Zoom and dive in your data to get specific information and details on data points thanks to our automated drill-down feature.
Improve and Scale
Build a software development data ecosystem that seamlessly ties in with the future of your organization.
Keypup's ready-to-use templates can help monitor key aspects, such as your team’s performance, development efficiency, quality and delivery, resource planning, processes, and more. A shift in mindset can enable you to become a data-first engineering organization and drive company growth.
Sign-up and accelerate your engineering organization today !
