Turning a TAR Skeptic into an AI Ambasador

Turning a TAR Skeptic into an AI Ambassador

A boutique litigation client of Acorn Legal Solutions had a complex dataset of medical documents relevant according to sender. The client was skeptical of the software because it was hard to differentiate between the documents due to language overlap.

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Challenges

•        Docs were non-searchable with inconsistent OCR

•        Traditional review unable to narrow dataset by sender

Solution

Reveal's NexLP's AI was far superior at identifying hot documents because of its entity extraction. Unlike traditional methods, Reveal's NexLP AI had the capabilities to identify the documents by sender, which was the differentiator. This allowed Acorn to overcome a seemingly unavoidable roadblock in their workflow.

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Results

The model was able to stabilize quicker due to acombination of coverage queue and better prediction models in Reveal's NexLPAI. Although the documents had no metadata and were not searchable, thetechnology's capabilities allowed it to look at the documentsin a way a person would. The client had its faith in TAR restored as well as trained AI Models that could be applied to future matters.

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