Zeitview Improves Recall by 12% and Doubles Data Throughput Using Encord

Zeitview, a provider of advanced inspection solutions for the energy and infrastructure sectors, significantly improved performance of their rooftop penetration detection model by focusing on high quality data delivery. 

We spoke with Jonathan Lwowski, Head of AI/ML, and Conor Wallace, Machine Learning Engineer, to understand how leveraging Encord helped improve data quality, tightened their feedback loop, and accelerated the deployment of machine learning models into production. 

Key results

Initial training data for Zeitview's rooftop penetration detection models suffered from quality issues. The first dataset was labeled by 15 external contractors using a third-party tool, resulting in inconsistent annotations and suboptimal model performance.

Zeitview improved data quality using the Encord platform: 

• Moved from external contractors to a smaller, specialized 5-person internal labeling team
• Implemented robust QA workflows for systematic quality control
• Relabeled problematic data while simultaneously expanding the dataset

These changes yielded meaningful results:

• 3.67% boost in precision 
• 12.33% boost in recall 
• 2x increase in dataset size and throughput
• Reduced team size by ⅓

The recall improvement is particularly significant for Zeitview's inspection use case, representing a meaningful enhancement in detection reliability. 

How did Zeitview achieve these results?

Encord's platform enabled several workflow improvements:

• Consolidate labeling and QA operations within a single environment
• Implemented structured QA/QC workflows that integrated MLEs directly into the feedback loop
• Leveraged image similarity search to quickly curate high-quality datasets

As Jonathan notes, labeling data can be expensive and time-consuming if not done efficiently:

Intelligent data curation

For complex projects with no reference data – like rooftop penetrations – Zeitview now leverages image similarity search and image quality metrics to curate high-quality datasets. This approach allows the team to rapidly identify edge cases and ensure diverse training datasets. 

Automated QA

Zeitview moved from spreadsheet-based feedback to a three-round QA workflow built within Encord, integrating ML engineers directly into the review process. As label accuracy improves, they are able to gradually reduce human oversight.

Active learning

Zeitview is preparing to use Encord Active to close the loop between model predictions and data curation. The goal: trigger automated retraining when failed predictions are detected, ensuring continuous model improvement.

Summary

Zeitview integrated Encord’s platform to centralize their data operations and automate key parts of the annotation and feedback workflow, enabling greater efficiency and consistency across teams.

With Encord, Zeitview has established a foundation for trustworthy, production-ready datasets and continuous model improvement - delivering faster iteration cycles, higher label quality, and more accurate inspection models.