The use case: how UIPath deploys Encord
UiPath uses Encord to annotate and manage training data across image, video, text, and voice models. It used the platform to expand the training dataset for its table extraction model, with multiple simultaneous annotators and structured review flows.
More reported results
Figures published in Encord’s case study. They are not independently measured by usedby.
the team achieved more than a 4x reduction in error rate
Before, the model was performing at around 96–97% mAP on their internal dataset
In the customer’s words
What Encord is bringing to our team now is mostly headspace. Taking away the pain of starting a new project from scratch. We just delegated that away and focused on the model.Other AI tools used by UIPath
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UVeyeConfirmedGo 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
- Apr 28, 2026
- Last checked
- Oct 30, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 10x+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 UIPath.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- UIPath uses EncordChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
After using Encord to more than 10x the size of the training dataset, the team achieved more than a 4x reduction in error rate, pushing accuracy above 98% and close to 99%.