wavescan.sg valuation and analysis

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Title WaveScan – Electromagnetics based sensor solutions for smarter liveable
Description WaveScan provides an end-to-end wireless microwave & mm-wave based NDT sensor solution with AI enabled capabilities integrated in the cloud for smart predictive maintenance of infrastructure
Keywords Predictive maintenance, NDT, infrastructure
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WebSite wavescan faviconwavescan.sg
Host IP 50.62.172.113
Location United States
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wavescan.sg Valuation
US$1,968,156
Last updated: 2023-05-08 16:51:57

wavescan.sg has Semrush global rank of 5,377,779. wavescan.sg has an estimated worth of US$ 1,968,156, based on its estimated Ads revenue. wavescan.sg receives approximately 227,095 unique visitors each day. Its web server is located in United States, with IP address 50.62.172.113. According to SiteAdvisor, wavescan.sg is safe to visit.

Traffic & Worth Estimates
Purchase/Sale Value US$1,968,156
Daily Ads Revenue US$1,817
Monthly Ads Revenue US$54,503
Yearly Ads Revenue US$654,034
Daily Unique Visitors 15,140
Note: All traffic and earnings values are estimates.
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HtmlToTextCheckTime:2023-05-08 16:51:57
ABOUT US WHY SOLUTION TEAM TRACTION CONTACT Hire us! Electromagnetics based sensor solutions for safer, resilient, and sustainable cities. WaveScan Technologies About Us WaveScan is an A*STAR spin-off company specializing in the R&D of disruptive beamforming electromagnetics based smart sensor systems and advanced AI algorithms. Our proprietary technology builds upon nearly a decade-long R&D on microwave & millimeter-wave wireless sensors and is capable of high-resolution 4D sensing for non-destructive testing (NDT) applications. We provide an end-to-end AI-enabled asset inspection solution, addressing the specific needs of the built environment sector. Smart sensor systems, integrable with autonomous vehicles, along with a centralized AI-enabled analytics solution, are the future of active inspection systems for sustaining resilient infrastructure. At WaveScan, we envision safer, productive & livable cities by creating a predictive maintenance ecosystem through data-driven
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