# How Hudl uses Encord

**10x**: “10x annotation speed improvement on tracking data”

As published on [encord.com](https://encord.com/customers/hudl/), April 2026. Captured by usedby on 2026-10-07.

- Company: [Hudl](https://www.usedby.ai/companies/hudl.md)
- Tool: [Encord](https://www.usedby.ai/tools/encord.md)
- Teams: Applied Machine Learning, Data Science, Annotation

## What the story says

Hudl's Applied Machine Learning team uses Encord as the central annotation and data management layer in its MLOps pipeline, annotating sports video and sensor data for model training. Hudl's own models pre-label the data, a validation agent flags errors, and annotators and data scientists work together in one workflow.

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

> By integrating Hudl's own pre-labelling pipeline into Encord, use cases that previously required approximately 10 hours of manual annotation now take around 1 hour.

- **40%**: “With the validation agent in place, the same process is now 40% faster.”

> Encord has broken down barriers between annotation and data science. Pre-labelling has probably been the biggest game changer for us. We've seen a 10x speed improvement on some of our most demanding projects.
>
> Ben Irving, Senior Engineering Manager, Applied Machine Learning

## What usedby checked

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

- Checked: the figure 10x is printed word for word on the page, near the name of Hudl.
- Checked: the passage quoted above is copied word for word from the page, near the name of Hudl.
- Checked: the publication date is read from the page’s own metadata, never guessed.
- Checked: Hudl 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/hudl-encord · How we check: https://www.usedby.ai/methodology