Case Studies

National Real Estate Company Saves Time and Improves Accuracy with

Emily Perkins

Emily Perkins

Head of Content Strategy

AI-first invoice processing and data extraction enabled this company to significantly improve data predictions, accuracy, and volume.

March 20, 2024

min read

National Real Estate Company Saves Time and Improves Accuracy with

Industry: Real Estate

Employees: 350

Annual invoice volume: 60,000

ERP: Oracle Cloud

This US real estate company serves a national audience, offering online home buying, selling, and mortgage loans. With 350 employees, the notable brand also has many invoices to process each month related to real estate transactions and remodeling projects.

The challenge: Manual invoice scanning and data entry

An accounts payable (AP) team of 10-plus employees process approximately 5,000 invoices worth $8 million a month that require manual scanning and data entry, with a mix of PO-generated and non-PO invoices. Some invoices are related to property renovation projects, and one of the biggest pain points for the AP team is being able to code the right house project on the invoice to related renovations and utility costs. Then, they need to manually check against a renovation budget before the payments are made. Invoices were also being managed in spreadsheets, resulting in frequent errors and lost time.

The solution: AI-first invoice processing and data extraction

The controller at the real estate company wanted to invest in AI automation to improve the accounting process. And scalability was key — with continued invoice volume growth, they needed to leverage technology that would streamline processes, reduce manual work, and easily expand over time.

The team selected’s autonomous finance platform for invoice processing. By batch-scanning and importing invoices into, data is now ingested and processed on behalf of the team, making it faster and easier for them to perform budget checks against the invoices.

Since launch, the team has seen good improvements in GL and line-item level predictions, and accuracy, invoice processing volume, and user processing trends have all improved. Now, the AP team can more efficiently process 5,000 invoices a month at an average of 90% accuracy. Overall, leadership says they are highly satisfied with the product so far and have been happy with the level of customer support and service provided by the team. And, the team members using daily have ranked the platform highly when asked about customer satisfaction.

national real estate company

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