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Cherre Secures $30 Million in Series C Funding to Enhance Growth


Cherre, a real estate data management and intelligence startup, raised $30 million in a Series C funding round led by HighSage Ventures. Participating investors included Nuveen Real Estate, RXR (RADV), and principals of TA Realty. Existing investors such as Trustbridge Partners, Glilot Capital Partners, Intel Capital, and Carthona Capital also contributed to the round.

This funding will allow Cherre to further invest in expanding its data intelligence capabilities. New offerings will equip their clients with advanced tools to manage and improve data quality, gain process intelligence, and build a flexible operating model for future competitiveness.

“Cherre is on track to become the foundational data platform for the real estate industry,” shared Nicholas Appelo of HighSage Ventures. “We are excited to support L.D. and his team as they seize the opportunity to enhance data intelligence for real estate investors, lenders, and service providers.”

Following a year of significant revenue growth, the Series C capital will enable Cherre to scale rapidly and continue developing innovative data management and intelligence tools to meet its clients’ evolving needs.

“We empower real estate organizations to leverage data for flexible growth and scalability,” explained L.D. Salmanson, CEO and Co-Founder of Cherre. “The round enables us to invest further in tools that help our clients manage, consume, and leverage their data for insightful decision-making and sustainable competitive advantage.”

Cherre’s platform addresses the real estate sector’s need for increased data transparency and quality, offering solutions for data pipeline visibility, transformation, observability, and error remediation. It empowers clients to confidently use data for analysis, reporting, and decision-making.

The startup was founded in 2016 and based in New York City. Cherre collaborates with major data providers and service providers, building an ecosystem that supports clients across their data needs.


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