Improving Efficiency in the Construction Industry with Encord

CONXAI is building an AI platform for the Architecture, Engineering and Construction industries to contextualise different data and transform them into actionable insights. CONXAI, however, encountered challenges with optimizing datasets, reviewing labels, and managing large volumes of data with their in-house data annotation solution.

This is where Encord came in - CONXAI was looking for an end-to-end solution for data management and curation, annotation, and evaluation.

Introducing Customer: CONXAI

CONXAI’s goal is to help AEC teams perform better by organizing and making sense of the vast amount of data generated during different stages of construction projects.

They specialize in making data more useful, especially since a lot of project data often goes unused. Their ultimate aim is to help AEC professionals use AI effectively to improve efficiency and tackle challenges in their projects.

We sat down with Markus Kittel, AI Product Development Manager at CONXAI, to discuss his work overseeing the product roadmap, and their exciting plans ahead for the business.

Problem: Challenges in Data Curation and Management

CONXAI's approach involves working with large unstructured datasets, which leads to challenges in effectively managing and curating project data. Their initial reliance on their in-house solution for data annotation proved to be problematic as the volume of data increased.

Like many in-house tools, it was prone to frequent malfunctions, obscured the data it processed, and lacked mechanisms for reviewing annotations. Additionally, scalability was a major concern, as the in-house tool struggled to handle the increasing volume and complexity of project data.

Without a reliable and scalable data management system in place, they faced difficulties in optimizing datasets and analyzing data effectively. As a result, CONXAI recognized the pressing need for a comprehensive solution that could streamline its data curation and management processes, enabling it to unlock the full potential of AI-driven insights within the AEC industry.

CONXAI were also in need of a solution where data security took precedence, enabling data to remain within CONXAI servers and be accessed via an API or SDK.

Solution: Encord Provides a Unified Platform for Data Curation and Management

“With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,” Markus says.

To address these challenges, CONXAI explored various annotation tools. They were searching for a single platform that could handle data curation and management seamlessly. Encord's Annotate and Encord Active emerged as the ideal solution, offering a comprehensive platform to streamline CONXAI’s operations.

As Markus says “We connect Encord Active with our large dataset and then use metrics to prioritize building a collection of images. This collection is then sent to Encord Annotate for labeling images in preparation for training. And all this without the data leaving our server.”

Result: 60% Increase in Labeling Speed

With the adoption of Encord into the data pipeline, CONXAI witnessed significant improvements in its data management processes. Encord facilitated the transformation of unstructured data into labeled, training-ready, datasets. The intuitive interface of Encord's Annotate tool simplified the annotation process for CONXAI's team, while also providing robust label review capabilities. Moreover, Encord's Active platform allowed CONXAI to efficiently curate and evaluate their datasets.

CONXAI was able to curate over 40k images with Encord Active. They were then able to efficiently evaluate and prioritize these images based on metrics, facilitating streamlined data management and enhanced decision-making processes within their operations.

CONXAI were able to contribute to Encord’s product roadmap by identifying that mapping relationships between labels in their ontology would enhance model performance. The Encord team were able to deploy this functionality, resulting in an improved user experience for CONXAI.

Overall, using Encord led to enhanced robustness, simplified data pipelines, and a remarkable 60% increase in labeling speed compared to CONXAI's previous in-house tool. This demonstrates how adopting an end-to-end platform with annotation, curation, and evaluation capabilities provides the best solution for computer vision teams.