Project Streamline

Sector

Recursive-built AI for government and administration

Type

ML

AI Type

Custom Machine Learning Models (ML)

Project Delivered

Case Study

GNNGPUML TRAININGDATA ANALYSISHYBRID MODELSPHSYCIS MODELLINGDATA PRE-PROCESSING

Proprietary AI model for detecting potential leaks in metropolitan water pipelines.

This project aims to build a machine learning model capable of identifying potential leaks in city-wide water pipelines. By detecting issues early, the system reduces the manual effort needed for inspections and repairs, enabling faster maintenance, lower costs, and more sustainable water management.

"The combination of an engineering viewpoint and expertise in biophysics was a tremendous asset and a great source of reassurance."

Takeshi Kato

Chief Engineer Environment and Resources Division, Sumitomo Forestry

"The combination of an engineering viewpoint and expertise in biophysics was a tremendous asset and a great source of reassurance."

Takeshi Kato

Chief Engineer Environment and Resources Division, Sumitomo Forestry

"The combination of an engineering viewpoint and expertise in biophysics was a tremendous asset and a great source of reassurance."

Takeshi Kato

Chief Engineer Environment and Resources Division, Sumitomo Forestry