The Challenge
Building that kind of report manually is challenging. Mixing time domains — annual snapshots alongside a partial quarter — means you can’t just run a single automated query. Each slice requires its own configuration. Across three dimensions and two workforce metrics, that’s not one report but many, and stitching them together accurately takes time.
Chess.com had year-over-year headcount reports already in place. But Brendan wanted to go deeper: a wider time window stretching back several years combined with Q1 2026 year-to-date data; multiple dimensions across country, department, and top-level organization; and both total headcount and full-time equivalent (FTE) figures side by side, with average annual compensation layered in for good measure.
Chess.com had year-over-year headcount reports already in place. But Brendan wanted to go deeper: a wider time window stretching back several years combined with Q1 2026 year-to-date data; multiple dimensions across country, department, and top-level organization; and both total headcount and full-time equivalent (FTE) figures side by side, with average annual compensation layered in for good measure.
“I wanted to take a look myself and derive some insights, but Rippling AI had already evaluated the changes that occurred over time and called out key observations. ”
James co
Business Development
The Solution
Finzo had just launched, and Brendan decided to put it to the test. He started with a focused first prompt: “Create a report showing active headcount by country over time. I want end of year totals for 2023, 2024, 2025, and quarter one of 26.”
The response came back immediately and hit the mark. So he pushed further. He asked for the same data split by department. Then by top-level department. Then he added a new layer: could it pull FTE alongside headcount and calculate average annual compensation relative to FTE, accounting for both salaried and hourly workers? Each time, Rippling AI delivered. The whole chain of prompts — including the thinking between them — took just a few minutes.
The Impact
The reports themselves were valuable. But what stayed with Brendan was what came alongside them. Without being asked, Rippling AI analyzed the changes in Chess.com’s workforce over the years and surfaced its own observations: shifts in hiring concentration by country, talent specialization patterns in certain roles, periods of heavy investment into core product teams. Strategic context that Brendan would normally have needed to derive himself — after the data was already in hand.
“I wanted to take a look myself and derive some insights, but Rippling AI had already evaluated the changes that occurred over time and called out key observations. ”
James co
Business Development
