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SewerAI utilises AI to detect sewer pipe issues

Jun, 18, 2024 Hi-network.com

Sewage failures exacerbated by climate change and ageing infrastructure are becoming increasingly costly and common across the United States. The Environmental Protection Agency estimates that nearly$700 billion is required over the next two decades to maintain existing wastewater and stormwater systems. In response to these challenges, Matthew Rosenthal and Billy Gilmartin, veterans of the wastewater treatment industry, founded SewerAI five years ago. Their goal was to leverage AI to improve the inspection and management of sewer infrastructure.

SewerAI's AI-driven platform offers cloud-based subscription products tailored for municipalities, utilities, and private contractors. Their tools, such as Pioneer and AutoCode, streamline field inspections and data management by enabling inspectors to upload data and automatically tag issues. That approach enhances efficiency and helps project managers plan and prioritise infrastructure repairs based on accurate 3D models generated from inspection videos.

Unlike traditional methods that rely on outdated on-premise software, SewerAI's technology increases productivity and reduces costs by facilitating more daily inspections. The company has distinguished itself in the competitive AI-assisted pipe inspection market by leveraging a robust dataset derived from 135 million feet of sewer pipe inspections. This data underpins their AI models, enabling precise defect detection and proactive infrastructure management.

Recently, SewerAI secured$15 million in funding from investors like Innovius Capital, bringing their total raised capital to$25 million. This investment will support SewerAI's expansion efforts, including AI model refinement, hiring initiatives, and diversification of their product offerings beyond inspection tools. The company anticipates continued growth as it meets rising demand and deploys its technology to empower organisations to achieve more with existing infrastructure budgets.

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