Infrastructure tech firm Kodifly raises $750k for LiDAR projects

The startup's founders were previously funded by Laidlaw's scholarships.

Patrick Rhys Atack July 01 2024

Hong Kong-based transport management tech startup Kodifly said it has raised $750k from the investment arm of philanthropic education fund Laidlaw Foundation. 

The cash injection from Laidlaw Scholars Ventures will be used to bring the tech and data analysis company’s hardware and software solutions to market, it explained. 

“This funding will support Kodifly’s productisation of its solutions, which use machine learning to create digital replicas of environments using LiDAR and real-time video analytics within urban environments.” 

The firm’s founders said its combination of camera and LiDAR, and its proprietary AI models will improve “urban mobility and infrastructure resilience.” It is pitched to infrastructure managers across road, rail and even airports. 

The firm said it would use the investment - its first external contribution - to expand from its existing work for the rail industry, and said it will target expansion outside of Hong Kong to the UK and US. 

Henry Wong, co-founder and CEO of Kodifly, expressed his enthusiasm: “This investment from Laidlaw Scholars Ventures is a significant milestone for Kodifly. It will enable us to develop our AI technologies further, productise our offering and expand our market presence, ultimately helping operators deliver efficiency in their infrastructure management to enhance safety and reduce maintenance costs, by transitioning to smart city technology using 3D perception and analytics.”

Both Wong and his co-founder Saad Shahid Anwel benefitted from the Laidlaw Foundation scholarships when they attended the University of Hong Kong. 

Niall Santamaria, chief investment officer at LSV, said: “Henry and Saad are exceptional entrepreneurs who were early to identify the opportunities and potential of spatial data. The models they are building have the potential to automate the detection and analysis of any anomaly, and while their success in the transportation sector is evident, the data they are interpreting has far broader applications, with vast opportunity.”

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