Engineering performance baseline
Scores performance from commit history and compares current output with the same team’s pre-AI year, so teams can see whether they are actually faster rather than just busier.
Navigara AI ROI
Navigara AI ROI measures engineering performance from commit history and turns AI spend into a capacity-and-roadmap view of return. It helps teams explain what AI produced, what it cost, and how much of it supported named objectives.
What Navigara AI ROI does
Navigara AI ROI is a product for measuring whether AI actually made engineering teams faster, and what that speed cost. It turns commit history into performance scores, compares those scores with a pre-AI baseline, and expresses the gain as capacity added rather than a vague productivity claim.
The product then connects that added capacity to AI spend and roadmap delivery. In the Pro plan, teams can break spend into work types, attribute spend to roadmap objectives through Jira or Linear, and translate the result into a monthly ROI statement that finance and engineering can read in the same terms.
Core capabilities
Capacity measured in engineer-equivalents
Converts throughput into capacity added, using ETV and commit-level grading to express gains as engineer-equivalents instead of a raw percentage.
Work-type spend breakdown
Breaks AI spend into work categories such as features, maintenance, tests, docs, and fixes, making the bill readable by type of work rather than as one lump sum.
Roadmap alignment tracking
Connects spend to roadmap objectives through Jira or Linear so teams can see how much of the AI bill supported named goals, off-roadmap work, or no stated outcome.
Model routing for token spend
Includes a smart model router that sends tasks to the cheapest model that can meet the quality bar, with policy based on task shape, blast radius, and team preferences.
Reporting and benchmarking
Provides weekly, monthly, and quarterly reporting, plus team and developer breakdowns and peer-cohort benchmarking for organizations that need recurring performance review.
Common ways teams use Navigara
Establish an AI performance baseline
Use the product when you want a baseline before or after an AI rollout. It compares the current team against the same team’s pre-AI year and turns the change into capacity, so the team can judge whether AI changed throughput in a meaningful way.
Explain AI spend to finance
Use Pro when finance or leadership asks what the AI budget produced. Navigara attributes spend to work type and prices it against graded output, which helps answer whether the bill translated into features, maintenance, tests, docs, or fixes.
Trace spend to roadmap objectives
Use the roadmap alignment flow when you need to distinguish roadmap work from unplanned effort. The product connects to Jira or Linear and shows how much spend was tied to named objectives, aligned but off roadmap, or not tied to a stated outcome.
Run regular engineering reviews
Use the reporting views when you need a recurring update for a team, department, or leadership review. The product offers per-developer and per-team breakdowns and weekly, monthly, and quarterly reports.
Deploy for regulated environments
Use the deployment options when security or data residency matters. Enterprise supports either an on-prem collector with a cloud front end or a fully on-premises setup, while keeping source code inside your perimeter.
Pros and Cons
Pros
- Measures engineering output from commit history instead of relying on self-reported AI usage.
- Shows AI impact as capacity added, which makes the result easier to discuss with leadership and finance.
- Separates spend by work type and by roadmap alignment, giving a clearer view of where AI-generated work went.
- Offers tiered plans from a free trial to paid measurement and ROI analysis, with public pricing for non-enterprise options.
- Supports both cloud SaaS and enterprise deployment models, including on-prem collector and full on-premises options.
Cons
- The public pages do not fully document all integrations or workflow details beyond Jira and Linear for roadmap alignment.
- The free and lower-cost plans cover performance measurement, but the full ROI statement, roadmap alignment, and spend attribution require Pro.
- Enterprise deployment, security, and compliance details are customized and not fixed in the public pricing page.
FAQ
What is the difference between Measure and Pro?
The product measures engineering performance from commit history, then combines that with AI spend and roadmap alignment in the Pro plan. Measure focuses on capacity added and performance trends, while Pro adds spend attribution, Jira or Linear alignment, model routing, process checks, and benchmarking.
Is Navigara cloud-only?
No. Both Measure and Pro are cloud SaaS plans. Enterprise is the option for on-prem collector, full on-premises, or air-gapped deployment.
Does Navigara offer a free trial?
Yes. The pricing page says the free Explore trial lasts 14 days and includes up to 1,000 pull requests analyzed, with full platform access during the trial and no charge during that period.
Does source code have to leave our servers?
Navigara reads commit history and does not require shipping source code to the cloud for Enterprise deployments. The enterprise options include a cloud SaaS setup with an on-prem collector or a fully on-premises deployment.
Which integrations are supported?
The source explicitly mentions Jira and Linear for roadmap alignment, and it refers to connecting commit history and repositories for performance analysis. Other integration details are not specified on the pages provided.
Quick Facts
- Category
- Developer Tool
- Primary use
- AI ROI and engineering performance measurement
- Data basis
- Commit history and graded output in ETV
- Roadmap tools mentioned
- Jira, Linear
- Deployment options
- Cloud SaaS; Enterprise with on-prem collector or full on-premises
- Pricing model
- Free trial plus paid per-developer plans; Enterprise custom
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