Rippling launches Rippling Data Cloud, unifying employee identity across tools to simplify engineering metrics and cut AI token costs

We launched Rippling Data Cloud today - an all-in-one rebuild of the modern data stack, with AI deeply integrated throughout.

Why would you want an org-and-employee-centric data stack? Well, here’s how I used Rippling Data Cloud to help with token burn and cut AI slop. 1/

Ten years in, Rippling is still shipping big new things.

If you're an enterprise customer, this is the safest kind of company to buy from. They're energetic enough that you know you'll get the latest tech, but you also know they won't just disappear.

I asked Rippling to build a dashboard to flag inefficient spend. It joined org data with claude spend, github pull-request and code-reviews, and perf management data, and one-shotted an analysis that changed the way we are overseeing token spend internally 2/

One telling insight – by looking across GitHub pull request data and Claude spend – Rippling AI identified engineers with high AI spend, whose peers frequently asked them to re-do their work in code reviews 3/

No single metric can measure engineering output. But a constellation of signals helps. The challenge is stitching data across systems where identities don’t match (“coder3” on GitHub vs. “adam@yourco.com”). Rippling’s employee-centric data model makes those joins trivial. 6/

Important to call out that Rippling AI is building real BI, not just svg images - you can drill down to see individual records, apply global filters and parameters, and share dashboards with either the owner’s or the recipient’s data permissions.

🔥

@parkerconrad 💪 relentlessly shipping

This is where enterprise AI is headed. Not another chat interface, but AI woven into every workflow so the product simply feels better. 🪄

“Spend per pull request” is interesting but not a great measure – our top and bottom performers are roughly tied for the lowest token spend per pull request. Neither is “total spend” something you want to drive down – our highest performers also have the highest total spend. 4/

High-spenders with few pull requests are immediately interesting – but only if you cut the data by level. If you’re a VP, this might make sense. For an IC, it’s worth further investigation. 5/

Is Rippling going to eat every SaaS category?

@parkerconrad 🫣 not me

@parkerconrad the slop isn't the model, it's the data going into it. clean inputs beat fancy algorithms every time.

@parkerconrad AI现在最大的问题是：说得比做的好听。真正能落地的项目，远比融资PPT里的少。 「我们今天推出了 Rippling 数据云——现代数据栈的全面重构，AI 深度集成其中」

@paulg That's exactly who you want to buy from..stable enough to trust with your business, hungry enough to keep earning it.

@parkerconrad This is how data *should* work. Clean, fast, no fluff.

@paulg Thats a solid take. Ten years of shipping means they've got the staying power without getting stale.

@paulg A sign the founder is pushing the pace and injecting risk!

@parkerconrad Bringing full workforce context to AI powered BI is going to be a game changer 🚀🚀