Papaya Global finds a repeatable path to AI transformation with Reindeer

Papaya Global is a $4.5B Workforce OS. As a workforce management solution, they manage payroll for thousands of workers across dozens of countries through local partners. Running a business like this creates backoffice complexity, including high-volume workflows that are hard to document and expensive to run manually. Papaya wanted to transform these operations with AI, but needed a partner who could handle messy processes without months of setup. 

They started with two workflows: payslip mapping and invoice coding. Reindeer built agents for both, processing data in any format and flagging uncertain cases to human experts. This resulted in:

• 50% reduction in manual payslip review
• 97% of invoices automated

"We wanted to grow the side of AI that focuses on operational processes. Now we know we can take a messy process, hand Reindeer a short description, and have it running in weeks. It's a repeatable process that's already changing how we're thinking about other workflows." 

— Yael Hoze, VP Product Management, Papaya Global

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Company

Papaya Global is a $4.5B Workforce OS that unifies payroll, HR, finance, and payments for global organizations. As a global workforce management solution, Papaya manages a network of local partners across dozens of countries, handling payroll for thousands of workers on behalf of their clients.

Challenge

Papaya has backoffice workflows they wanted to automate, but the ones that cost the most time are also the hardest to hand off to software. The formats are inconsistent, the rules shift by customer, and the logic that actually drives decisions was never written down.

Payslip mapping is one such workflow. Payroll providers send over thousands of payslips a month, and no two look the same. Someone has to extract the data from each one, figure out which employee it belongs to, and route it to the right place. Before Reindeer, half of them required manual review because names didn't match the database or the same employee appeared twice with different emails.

Invoice coding had the same problem. When invoices arrive, they need to be loaded into NetSuite with the correct codes. But an invoice for consulting services might get coded one way for a European client and another way for a US-based one. Those rules lived in people's heads, not in any documentation.

Solution: Payslip Mapping

Reindeer built an agent that handles the payslip workflow end to end. Before Reindeer, human experts were manually reviewing almost 50,000 payslips a month. Now when payslips come in, Reindeer extracts the relevant fields from each one: name, pay period, dates, employee number, tax ID. This data is extracted across different formats and languages. It also catches cases where multiple payslips are bundled into a single PDF and separates them correctly, or when a payslip was issued to the wrong employee.

From there, the agent matches each payslip to the right employee in Papaya's database. This sounds simple, but often the name on the payslip doesn't match the name in the system, or the same employee appears twice with different email addresses. 

When that happens, the agent flags the case to Papaya’s team, shows them the candidates, and it remembers their selection for next time. The system also catches bad uploads like corrupted files or documents that aren't actually payslips, so those don't slip through the cracks. The agent then remembers the human guidance, so the next month, the same issue won’t get flagged again

Solution: Invoice Coding

Reindeer built an agent that handles invoice coding end to end. Before Reindeer, Papaya's finance team was manually extracting data from invoices, loading it into NetSuite, and assigning the right codes. The problem was the rules shift by customer and contract type. An invoice for consulting services might get coded one way for a European client and another way for a US-based one or a recurring charge might follow different logic than a one-time payment. None of this was documented.

Reindeer built the initial model from a batch of invoices Papaya's team had already processed. The system analyzed how they extracted data, which fields they populated, and how they assigned codes. That was enough to handle about 80% of incoming volume immediately.

The other 20% is where most automation falls apart. Instead of guessing, the agent flags cases it's uncertain about and routes them to Papaya's finance team. When someone makes a correction, that knowledge feeds back into the model. Over time, coverage climbed from 80% to 97%.

Results

Papaya set out to transform their backoffice with AI, and now they have proof it works. Two different workflows, two different problems, same result: automation that handles messy data and stays accurate over time.

• 50% reduction in manual payslip review
• 97% of invoices automated
• 10,000+ payslips processed monthly
• One short document to train the payslip mapping model
• Less than 50 samples needed to train the invoice coding model

Reindeer is built for the kinds of processes that seem impossible to automate because they’ll never be 100% documented. Papaya is already identifying which backoffice workflows to bring to Reindeer next. 

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