Leverton, which saves property companies time by using machine learning to crunch lease data, has won a significant contract with the largest central London-focused REIT.
Derwent London owns and manages an investment portfolio of 5.5m sq ft The investor-developer will use the technology to streamline the process of managing its lease portfolio.
The pair said they had agreed a multiyear deal to employ Leverton’s data extraction software and services “to accelerate the way in which the company accesses, interacts with and manages data across its entire portfolio.”
Jennifer Whybrow, head of financial planning and analysis at Derwent London, said: “We are pleased to be working with LEVERTON to speed up and improve the way we capture key commercial lease terms. We are always looking for ways to improve our business through innovation and leveraging the power of AI to achieve operational efficiencies fits with that ethos.”
Leverton’s technology automatically extracts and structures key data points from corporate and legal documentation, on average saving 60-70% of the time it takes for manual data abstraction, the firm said.
Richard Belgrave, chief revenue officer of Leverton, added: “We are thrilled to be working with Derwent London, a business that is continuing to pave the way in the industry by offering their customers the best services, while also working to find ways to improve their internal processes that add value to their team through the adoption of new technology that leads to better data management, and ultimately, smarter decisions.”
Leverton has more than 100 clients including in corporate and legal markets. The company has offices in New York, London, and Berlin.
Publicly listed Derwent London owns 87 buildings in a commercial real estate portfolio predominantly in central London valued at £5bn at 30 June 2018. Landmark schemes in its 5.5m sq ft portfolio include White Collar Factory EC1, Angel Building EC1, The Buckley Building EC1, 1-2 Stephen Street W1, Horseferry House SW1 and Tea Building E1.
The article was originally published on PlaceTech.
Author: Paul Unger