Reveal Acquires Onna

Trustpoint.One uses AI to find more data in less time

Litigation team
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Trustpoint.One, one of the largest integrated legal service providers in the US, was tasked by outside counsel for a Fortune 10 company to leverage Reveal’s NexLP AI models to efficiently uncover critical case details after initially spending over 11,000 hours and only finding 11 hot documents among 500,000.


  • Too much time and money had already been spent with little to show for it
  • Expanding search with outside counsel’s default platform yielded no new results


Outside counsel, unhappy with the results they had been achieving, brought in Trustpoint’s AI department. They asked Trustpoint to continue with the firm’s default platform, trying threading, near duplicate analysis, domain parsing and exports, “from” field pivot exports, and structured analytics. When those efforts yielded no additional hot documents, the firm agree to let Trustpoint take a different approach.

Trustpoint turned to Reveal’s NexLP AI, its cutting-edge artificial intelligence technology, and its reusable AI models.  Trustpoint used NexLP AI to take advantage of methods such as Suspected Mass Sender, Thread Searches, Cluster, and Super Categories to quickly make smarter, faster, and more informed decisions about the documents.


On day 1, Trustpoint found 3 now hot documents, identified additional new potentially interesting documents, created new criteria for finding further hot documents, and developed suggestions for improved search. As Trustpoint continued to evaluate the documents using Reveal’s NexLP AI, they determined that the date ranges used by counsel were too narrow; improved their identification of hot and responsive documents; more efficiently batched documents for review; and more effectively culled junk for the data set.

By day 12, Trustpoint completed analysis of the initial 500,000 documents, finding 19 new hot documents, accomplishing more in a couple of weeks than counsel’s traditional process had delivered after months of review.

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