Navigara is a performance analytics platform for AI-native engineering teams, measuring engineering output per commit and pull request and tying it to business priorities. The platform scores work using Engineering Throughput Value (ETV), a proprietary unit that evaluates complexity, architecture, and decay from commit history alone. It also tracks AI ROI, token spend, roadmap alignment, and process health to help teams identify and fix their actual bottlenecks.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering leaders often rely on gut feeling, surveys, or story points to assess team performance, which fails to capture the true value of work delivered. As AI accelerates coding, teams struggle to measure whether output is actually improving, whether AI investments are paying off, and which stage of the development loop—coding, reviewing, or product definition—is the real constraint.
Solution
Navigara provides a performance layer for AI-native engineering that measures engineering output directly from commit history and pull requests. The platform uses a proprietary unit called Engineering Throughput Value (ETV), which evaluates each change based on complexity, engagement, architecture placement, decay, and multiplier factors. ETV is calculated per file and per merged commit, categorizing work into features, maintenance, tests, docs, or fixes, without relying on self-reports or project management tools. Navigara also tracks AI ROI by measuring engineering before and after AI adoption, monitors token spend across models, scores roadmap alignment per team, and runs nightly process checks to flag bottlenecks in coding, reviewing, and product specification health.
Target Audience
Primary customers are engineering leaders, CTOs, and directors of engineering at mid-to-large technology companies that have adopted AI coding tools and need objective, data-driven visibility into engineering performance, AI ROI, and process bottlenecks.
Features
- ETV (Engineering Throughput Value) measurement engine that reads commit history like a senior engineer, scoring complexity, engagement, architecture, decay, and multiplier factors per file and per merged commit
- AI ROI reporting that measures engineering performance before and after AI adoption, expressed as capacity gained without hiring
- Token Spend Intelligence that routes each task to the cheapest capable model and prices all AI usage with no stated objective
- Roadmap Alignment scoring that measures how much each team contributed to each business priority, with context health audits for objectives, epics, and tickets
- Process Checks that run nightly to monitor coding, reviewing, and product stages, with an AI review agent that provides first-pass reviews for pull requests over 400 lines
- CapEx & OpEx reporting that derives audit-defensible software capitalization directly from code
- AI Transformation radar scoring across five pillars, per team and per repository
- Adaptive Token Limits that move each team's AI budget based on delivered value within a fixed total