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PredictAP Raises $8 Million in Series-A Funding To Automate Accounts Payable for the Real Estate Industry

PredictAP Raises $8 Million in Series-A Funding To Automate Accounts Payable for the Real Estate Industry

January 17, 2024 Craig Etkin

As the company experiences rapid customer growth, this raise will support expansion as the leading Vertical SaaS platform across the U.S. and international markets

January 17, 2024 01:55 PM Eastern Standard Time

BOSTON–(BUSINESS WIRE)–PredictAP, a machine-learning enabled invoice ingestion & coding solution for real estate accounts payable (AP), today announced the close of its $8-million series A financing round. The round was led by leading real estate technology venture capital firm RET Ventures, with additional participation from Wise Ventures.

“The market offered no good solution that also catered to real estate or could be easily implemented. We built PredictAP to solve this specific issue while offering seamless implementation to offer maximum impact with minimal effort.”

Founded in 2020 by David Stifter and Russell Franks, PredictAP was developed as a fintech solution to automate the ingestion and coding of invoices for real estate accounting departments. Large real estate companies receive thousands of invoices a month, each of which has to be coded through a several-minute process that requires manual data entry. PredictAP developed an AI-based solution that leverages historical invoice data to code new invoices with accuracy and efficiency, after which an accounting professional can confirm the information in a matter of seconds.

The only automated AP solution that performs both invoice ingestion and coding, PredictAP reduces the amount of time needed to process each invoice by 80-90%. PredictAP’s machine learning-powered solution also improves the accuracy and consistency of invoice coding. The platform integrates easily with Yardi, the largest property management software, providing users with an end-to-end AP workflow automation solution that results in significant time and cost savings for the AP process.

“Manual data entry for invoice ingestion and coding takes time away from higher value work for busy accounting departments and property management offices,” said David Stifter, CEO and founder of PredictAP. “For most real estate firms, the institutional knowledge needed to code invoices correctly is not captured in an institutional way, making this work hard to delegate and even harder to scale. Downstream issues like missed or duplicate payments, frequent reclassifications, and no visibility into spend turn into big headaches for controllers and CFOs,” he added. “The market offered no good solution that also catered to real estate or could be easily implemented. We built PredictAP to solve this specific issue while offering seamless implementation to offer maximum impact with minimal effort.”

Since its launch, PredictAP has rapidly grown its user base, with the company now supporting over 50 real estate companies, who jointly process approximately over 2 million invoices per year. The fintech solution is utilized by major real estate companies including Bridge Investment Group, Garden Homes, The RMR Group, Starwood, and CA Ventures.

“As PredictAP reaches an inflection point, with rapidly accelerating customer growth in 2023, this raise will facilitate our expansion into new regions with our Yardi integration while growing our integration roster with other real estate focused platforms like Nexus, RealPage, and Entrata,” said Russell Franks, president and co-founder of PredictAP. “After proving ourselves with customers across all asset classes, we’re eager to deliver the same time savings and rapid time to value to new markets.”

“PredictAP’s powerful technology is one of the most successful uses of artificial intelligence that we’ve seen in the real estate sector, and the impact it is already having is immediately apparent to its users,” said RET Ventures Partner John Helm. “The company was an exceptional investment opportunity because of its accelerated sales cycle; the firm goes through the sales process, closing and technology deployment as quickly as any company we’ve seen. With approximately a billion invoices being processed globally each year, PredictAP is still in the early stages of its growth trajectory, and we’re excited to support the company as it scales in the years ahead.”

About PredictAP

PredictAP is an AI-powered invoice ingestion and coding solution purpose-built for real estate accounts payable. It saves time by eliminating manual data entry and gets invoices fully coded in seconds. PredictAP integrates with leading AP automation solutions like Yardi, and helps real estate AP teams scale capacity, improve coding compliance, and reclaim valuable time.

Founded in 2020, the Boston-based company works with real estate companies spanning residential, commercial, and senior living asset classes. Learn more at https://www.predictap.com/.

About RET Ventures

A leading real estate technology venture capital firm, RET Ventures elevates real estate innovation by investing in cutting-edge technologies out of its core venture funds and a Housing Impact Fund. RET works in partnership with its base of more than 50 Strategic Investors who own and manage over $600 billion of real estate assets, with a particular focus on multifamily and single-family rental real estate. In these asset classes, the group includes some of the largest REITs and private real estate owners, operators and developers, who together control approximately 2.5 million rental units.

Through its deep expertise and network, RET has created a unique real estate innovation ecosystem that delivers significant value to the companies it backs, providing them with access to thought leaders, development partners and ongoing strategic guidance. For more information, please visit www.ret.vc.

Contacts

MEDIA
Shlomo Morgulis
Antenna | Spaces
retventures@antennagroup.com

(c)2023 Business Wire, Inc., All rights reserved.


Venture Capital
Boston, Business Wire, Massachusetts, PredictAP, Venture Capital

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