Developer Performance: Measured From Code, Not Self-Reports

You have 20 engineers. You know maybe 5 of them well enough to evaluate their actual output. The rest? You rely on managers who rely on standups who rely on what people say they're doing. It's a game of telephone where the truth degrades at every step.

Jira tells you who closes tickets. It doesn't tell you who writes quality code, who unblocks the team through reviews, who refactors the critical path, or who ships features that actually work in production. The engineer who closes 30 trivial tickets looks like a top performer. The one who rewrites the payment service and saves the company from a compliance disaster looks average.

Performance management without code-level visibility is theater. You end up rewarding the wrong people, missing the ones who matter, and making headcount decisions based on narratives instead of evidence.

How InteliG Solves This

• → Measure from Git, not Jira. InteliG analyzes commits, PRs, reviews, and deployments to build a multi-dimensional view of each developer's contribution. No self-reporting. No manager interpretation. Just code.
• → Quality scores that mean something. Not lines of code — impact. InteliG evaluates code complexity, review thoroughness, deployment frequency, and how often code ships without breaking things.
• → See contribution patterns over time. Is a developer ramping up or declining? Are they concentrated in one repo or contributing across the org? Are they doing only feature work, or also investing in quality and infrastructure?
• → Distinguish human and AI contributions. Half your code may soon come from AI agents. InteliG separates human output from machine output, so you know what your team is actually doing versus what the tools handle.

Questions You Can Ask Cognis