Scope Computer Vision Technologies

Scope Computer Vision Technologies

Revolutionizing a 1000 year old process of inspecting rope for damage through applied deep learning.

HQ location
Houston, United States
Launch date
Employees
Enterprise value
$24—35m
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DateInvestorsAmountRound
N/A

€0.0

round
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N/A

Growth Equity VC
Total Funding000k
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VisionByScope is a startup that is revolutionizing the traditional process of inspecting rope for damage by leveraging deep learning technology. The company operates in the safety inspection market, specifically focusing on critical rope applications. The current standard practice worldwide for determining rope safety is manual visual inspection, a process that is subjective, qualitative, and fraught with risk. VisionByScope aims to eliminate these risks by providing automated inspection systems that offer a more objective, quantitative, and consistent measure of rope quality.

The company's business model revolves around selling these automated inspection systems to operators who use ropes in critical applications. These systems use deep learning technology to identify and assess line damage, enabling operators to take data-driven actions to repair or replace failing lines before catastrophic incidents occur. This is done in real-time and with a level of accuracy that exceeds manual inspection. The software is continuously learning, improving its ability to predict rope damage and residual break strength (RBS) within 5% of the actual value.

VisionByScope's systems not only increase safety but also help operators comply with industry standards and regulations. They ensure that all synthetic ropes are consistently maintained to a safety factor and in good working condition. Moreover, these systems allow operators to inspect, assess, and manage their fleet across multiple locations simultaneously, saving time and labor costs compared to manual processes.

In summary, VisionByScope is a deep learning startup that provides automated rope inspection systems to operators in critical applications, enhancing safety, compliance, and efficiency.

Keywords: Deep Learning, Rope Inspection, Safety Compliance, Automated Systems, Real-Time Analysis, Data-Driven Actions, Residual Break Strength, Industry Standards, Fleet Management, Labor Cost Savings.

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