A Low Code Solution for a Leading Adhesive Manufacturer
About the Client
A leading adhesive manufacturer that has been a driving force in the industry. Renowned for various iconic brands, the company’s diverse portfolio extends to construction chemicals, art materials, and more. Their commitment to quality and customer satisfaction has solidified their position as a trusted partner for various industries. With a legacy of resilience and a focus on sustainable growth, the company continues to shape the landscape of adhesives, contributing to the success of big and small projects across the region and beyond.
Traditionally, manual inspections by trained professionals have been the primary method for identifying defects like cracks and leaks on the damaged surface. However, this approach is often time-consuming, resource-intensive, and prone to human error, leading to potential safety hazards and costly repair delays for the end users of the client’s services. The client was in search of an alternative solution that utilizes AI technology.
The main challenge was to build an automated and scalable solution while keeping the cost in check. The solution was expected to detect 13+ different types of defects. Thousands of users running checks for all different types of defects 24×7 would have led to heavy usage of cloud resources, resulting in high running costs. Thus, the deployed model was expected to execute only when there is an API hit during a specific time period of the day (business hours).
Considering GCP’s Machine Learning capabilities, Niveus recommended building an automated defect detection and scalable solution on GCP to identify defects on various surfaces, including cracks in parapet walls, leakage, and surface type. We proposed a low code (AutoML) solution to eliminate the need for domain knowledge and specific skills to identify the defects.