The use case: how Zeitview deploys Encord
Zeitview uses Encord to label and quality-check training data for its rooftop penetration detection models. It also uses the platform's image similarity search and image quality metrics to curate datasets, and runs a multi-round QA workflow that involves ML engineers in the review process.
More reported results
Figures published in Encord’s case study. They are not independently measured by usedby.
3.67% boost in precision
2x increase in dataset size and throughput
In the customer’s words
The most valuable feature to us is the automated QA workflow that forces the ML engineers to be more incorporated into the labeling process.Other companies using Encord
Customers across industries, with public evidence.
Archetype AIConfirmed
HudlConfirmed
OnsiteIQConfirmed
Pickle RobotConfirmed
Standard AIConfirmed
Thoro.aiConfirmedGo straight to the source
Based on Encord’s published case study. We check the public evidence; the results are reported by the vendor.
encord.com- Source published
- Mar 24, 2026
- Last checked
- Oct 7, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 12.33%Checked
The figure is on the page; its label is our wording.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Zeitview.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- Zeitview uses EncordChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
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.