# How UVeye uses Encord

**20-30%**: “Since implementing Encord, UVeye has seen around a 20-30% improvement in their data annotation rate”

As published on [encord.com](https://encord.com/customers/uveye-case-study/), June 2026. Captured by usedby on 2026-10-07.

- Company: [UVeye](https://www.usedby.ai/companies/uveye.md)
- Tool: [Encord](https://www.usedby.ai/tools/encord.md)
- Teams: Data Operations

## What the story says

UVeye uses Encord to annotate video sequences and multi-frame image groups of vehicle scans for its computer vision models used in automated vehicle inspection. The team uses it for object tracking, image grouping, review workflows and analytics to manage annotators and reviewers.

Summary written by usedby from the source page, in English. The figures are those of Encord and UVeye, not ours.

> Since implementing Encord, UVeye has seen around a 20-30% improvement in their data annotation rate, allowing them to deliver more training data within the same number of working hours while completing projects in shorter cycles.

> Encord has eliminated the need for manual tracking tables. In our previous platform, we had to rely on spreadsheets to ensure that annotators and reviewers weren't overlapping. With Encord the process is built into the workflow, allowing the team to focus entirely on the task without worrying about coordination overhead.
>
> Inbal Friedlander, Data Operations Team Lead at UVeye

## What usedby checked

We compared the story with its live page on 2026-10-07.

- Checked: the figure 20-30% is printed word for word on the page, near the name of UVeye.
- Checked: the passage quoted above is copied word for word from the page, near the name of UVeye.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: UVeye uses Encord. Confirmed line. Latest check across sources: 2026-10-07.
- Not checked: the result itself. We quote it; we did not measure it.

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Source: https://www.usedby.ai/case-studies/uveye-encord · How we check: https://www.usedby.ai/methodology