Parker Conrad wants you to believe that a huge chunk of data analytics belongs inside human capital management systems — a claim that conveniently positions Rippling, which started out as an HR software company, to compete directly with dedicated business intelligence tools.

The pitch is that the modern data stack — the galaxy of tools that companies currently jury-rig from multiple vendors — can be collapsed into one. Just moving data from your various business systems into a warehouse is itself a massive industry; that’s what companies like Fivetran and Airbyte do. Then you need somewhere to store and query it, like Snowflake, then something to transform and clean it, like dbt Labs, then a visualization layer like Tableau on top.

Conrad’s argument is that Rippling knits together all of that into one system and wraps it in something the others lack: a built-in understanding of your org, its ever-evolving reporting structure, and everything impacted when any metric moves up or down. That’s what Rippling Data Cloud, launching today, is designed to deliver.

To see it in action, Conrad shares his screen from his San Francisco office, and then offers a window into what Rippling found when it turned the product on its own workforce.

“There were employees doing things like, ‘Claude is so helpful for me — it analyzes my calendar and my email and puts together a plan for me,’” he says. “That person was spending at a run rate of $30,000 a year for this.”

No one was doing anything wrong, he’s quick to add, but the ROI simply wasn’t there. It’s the kind of finding that most companies currently have no way of surfacing.

He then shows me a live dashboard he’s built by simply asking Rippling AI to analyze his company’s most recent compensation review cycle — distributions of performance ratings, promotion rates by department, salary ratios, all of it drillable to the individual level. Then he pulls up another, this one cross-referencing support ticket volume from Salesforce with employee scheduling data — enough to show, at a glance, which teams are drowning and which aren’t. The enrollments team, he notes, is severely understaffed. The travel team has more than double the unresolved tickets of the platform team.

But the example Conrad seems most excited about is one closer to a preoccupation many executives share right now: AI token spend. He shows a dashboard combining data from Anthropic’s usage logs, GitHub pull request data, and Rippling’s own performance ratings to peer at which engineers are actually getting value from their AI tools and which are burning money without much to show for it.

“The high performers spend the most, which you would sort of expect,” Conrad says. But the dashboard also flags engineers with high spend and high peer rejection rates on code reviews — these are people whose colleagues are frequently asking them to redo something. “If your peers are telling you to go back and do this over all the time, maybe you’re just generating a lot of slop,” he says.

The analysis has already prompted Rippling to cut spending limits for certain employees. The product can also be configured to alert managers — or automatically shut off access — when employees blow past a spending threshold.

On the question of impact to Rippling’s own margins when customers exceed their token allotments, Conrad doesn’t get specific — “it’s kind of early,” he says — but brushes back the idea that Rippling is subsidizing customer usage. “We’re not losing money,” he says, adding that the goal is to keep it “as affordable as possible for customers.” The base SKU, bundled with Rippling AI, runs around $20 a month, with usage-based charges kicking in for heavier consumers. About 560 companies are currently using it, with new revenue from the product running at roughly $5 million to $7 million a month.

As for which AI models are actually powering Rippling’s growing AI suite, Conrad says the company has a new favorite at the moment. “We’ve actually moved a lot of stuff from Anthropic to OpenAI recently,” he offers, deeming OpenAI’s 5.5 model as “both better and more cost-effective” for what Rippling is doing. He”s also careful to note the balance keeps shifting and the company uses different models for different tasks.

Rippling Data Cloud is the most prominent launch this week, but it isn’t the only one. Earlier this week, the company also announced Business Banking, which offers a high-yield checking account and same-day payroll processing, a feature Conrad describes as eliminating the mental overhead of managing two timelines at once. Most payroll systems require processing two to four days in advance; Rippling’s banking product enables companies to run payroll on the day employees are paid, with changes accepted as late as 1 p.m. on payday.

It’s an elbow thrown into territory occupied by fintechs like Ramp, which just raised $750 million at a $44 billion valuation — nearly three times the $16.8 billion valuation Rippling’s investors assigned the company last year — and which has been positioning itself as the financial operating system for companies navigating AI costs. Conrad welcomes the comparison, noting that Rippling’s banking business is far smaller than Ramp’s currently but is “growing very quickly and doing extremely well,” and that “there are some advantages to centralizing all of this.”

Conrad says overall, Rippling is still roughly two years from cash flow positive, spending 45% to 50% of its revenue on R&D compared to the roughly 8% to 9% percent that public-market HR companies like Paylocity and Paycom spend. The cost of building everything in-house is the point, in other words, and the payoff is a system that can easily answer questions without pulling from four different vendor stacks to do it.

As for an IPO, Conrad makes it very clear he’s in no hurry, even with the window wide open right now. “The public markets have become this retirement community for slow growth companies,” he says, adding that he’s “not religious one way or the other,” even as it sounds very much the opposite. For now, he says flatly: “We are not going public. Not even with a ‘wink, wink,” he adds.