Navigara is an engineering performance platform that quantifies the impact of AI tools on developer capacity, roadmap alignment, and financial efficiency.
What the product does and how it is positioned
Navigara provides engineering teams with a framework to measure the tangible impact of AI adoption. By analyzing code commits and pull requests, the platform translates AI-driven productivity gains into engineer-equivalents, allowing organizations to assess capacity growth without increasing headcount.
Beyond capacity measurement, the platform offers visibility into how engineering efforts align with business priorities. It integrates with project management tools to track roadmap progress and provides financial insights, including CapEx and OpEx reporting and token spend intelligence for AI models.
Source-supported ways to use the product
Engineering leaders use the platform to demonstrate the return on investment for AI tools by calculating the cost per added engineer of capacity.
Teams connect engineering activity to business objectives to determine if AI-driven speed improvements are accelerating the delivery of planned roadmap initiatives.
Navigara evaluates engineering performance by grading every commit and pull request using a specific metric known as ETV. This approach moves beyond simple activity tracking to assess the actual work produced, categorizing it into buckets such as new features, maintenance, testing, documentation, and bug fixes.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
The platform measures engineering performance per commit and pull request, calculating the capacity added by AI tools in terms of engineer-equivalents rather than simple percentages.
Yes, the platform provides audit-defensible software capitalization reports for CapEx and OpEx, derived directly from code activity and engineering output.
Yes, Navigara categorizes work into types such as features, maintenance, tests, documentation, and bug fixes to ensure that AI adoption is driving value rather than just increasing maintenance overhead.
By integrating with Jira, the platform ties engineering output and AI spend to specific named objectives, allowing teams to see if initiatives are being delivered earlier due to AI-driven capacity.
This feature helps organizations manage AI costs by routing tasks to the most cost-effective models and providing visibility into total AI expenditure.
