Wie das einköpfige HR-Team von NinjaTech AI in Sekunden an globale HR-Daten kommt

The Challenge

Natalia Shreve is the Founding Recruiter & Director of People Operations at NinjaTech AI, a Silicon Valley-based start-up building general AI agents with dedicated cloud computers that enable automation of real work autonomously 24/7. With teams spanning Mountain View, Vancouver and Sydney, Natalia manages payroll in three different currencies, oversees recruiting across three time zones and handles every people operation the company needs – solo.

Efficiency isn’t a luxury in a setup like that. It’s the job.

The stakes of that reality came into focus one afternoon when Natalia was heading out the door for a doctor’s appointment. A message arrived from a member of the leadership team: they needed a complete workforce report – active and terminated employees alike, across all three locations, salaries included – for an investor packet. Immediately.

Pulling that report manually meant opening her pre-built census report, reconfiguring it to cover all three locations, adjusting the date range to capture the past 12 months, changing the status filter to include terminated employees and then – because Ninjatech AI runs payroll in three currencies – manually converting every salary figure into US dollars. Natalia estimated that process would take up to 45 minutes.

The Solution

Natalia had recently onboarded to Rippling AI, and she decided to see if it was up to the task. She typed a single prompt: “Pull a report of all employees who worked in the last 12 months, include location, job title and salary, indicate employment dates and whether they are active or terminated.”

Within moments, Rippling AI returned the complete report – along with a downloadable CSV file and a set of key insights she hadn’t asked for: total terminations over the past year, workforce distribution across all three locations, and every salary already converted to US dollars.

Natalia verified the numbers against what she knew from running biweekly payroll. Everything was accurate. She forwarded the CSV to her colleague with a quick note – “I verified this data is accurate” – and made her appointment. The entire process, from prompt to sent file, took roughly 100 seconds.

The Impact

When asked to distil what Rippling AI actually delivered in that moment, Natalia named three things:

Time savings. What would have taken up to 60 minutes of manual configuration was handled in under two minutes. A 30x improvement on a task that routinely competes with higher-priority work.

Internal responsiveness. The leadership team member got an accurate, investor-ready dataset almost instantly. In a start-up environment, that kind of responsiveness builds trust and creates leverage for a solo operator.

Accuracy. Because Rippling AI works directly within the platform’s live data, Natalia could verify the output against her own payroll knowledge instantly. No exports, no re-keying, no version mismatch. The data was right the first time.

Since that afternoon, Natalia has brought the same approach to Australian payroll runs – using Rippling AI to input contractor hours and tax amounts directly into the correct payroll fields, asking clarifying questions when needed rather than requiring her to know the exact UI location.

For a one-person HR team managing operations across three countries, that question changes everything. It means fewer support tickets, fewer minutes lost to configuration and more capacity to focus on the parts of the job that actually require a person – building a team, supporting a growing company and showing up for a doctor’s appointment.