The effort-based allocation model is the standout. Jellyfish translates raw development activity into an FTE-equivalent split across investment categories (Growth, KTLO, Support) based on actual effort signals rather than ticket counts — which is critical, because counting issues badly misrepresents real work. This is what made my monthly CEO and board reporting credible: leadership could trust an "X FTE went to Growth this month" figure because it reflected effort, not just Jira volume. The mapping of investment categories to our own strategy taxonomy and the month-over-month trend views also let me show direction over time with minimal manual assembly, and the delivery and AI-assisted PR metrics fed our AI-adoption reporting well.

The investment breakdown is what stands out most. As an IC, I rarely get visibility into how engineering effort is actually distributed across initiatives, but Jellyfish makes it easy to see how time is being spent across new features, bug fixes, and tech debt without anyone manually pulling that data together. The Jira and GitHub integrations just work, which means the data is always current and trustworthy. That alone saves a lot of back-and-forth during planning cycles.

Jellyfish provides insights into my PR cycle time and issue cycle time, while also allowing me to track my quarter-over-quarter growth. This helps me monitor my performance metrics effectively and present data-backed progress.The data is actually useful day-to-day, I can see where my time is going, how much is planned work vs interruptions, and where PRs are getting stuck in review. Found out 40% of my team's capacity was getting eaten by unplanned work. That's not something you notice until you see it in a chart.