SentioAI

Next generation Continuous Active Learning Predictive Analytics

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Why SentioAI?

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Has no need for seed sets

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Uses advanced modeling techniques to build and apply models to quickly find responsive documents for all tags

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Simultaneously trains multiple models (a model for each tag)

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Uses a proprietary workflow with unique capabilities that lets users train a software model by reviewing and tagging a small portion of the data set, and then use the trained model to automatically generate predictive results for the remaining documents

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Allows having only a few reviewers regardless of the size of a document set

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Performs real-time monitoring of model building

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Saves models for use in subsequent productions or matters/cases

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And many moreā€¦
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SentioAI uses machine learning to find relevant documents based on input provided by users. Analogous to a music streaming service "choosing" which songs a user will enjoy based on previous song selections, SentioAI uses document tags to train a ranking algorithm that orders relevant documents from most to least likely.

In addition to being able to train models using only a small data set, SentioAI's proprietary workflow enables having only a few reviewers regardless of the size of a document set. The software also detects documents that are likely misclassified by a reviewer and recommends verification.

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The software provides users with the ability to algorithmically eliminate human review of a large percentage of a document collection - often by as much as 90%, during the first-pass review, potentially reducing a company's document review and management expense by millions of dollars and achieving improved results in far less time.

If a seed has already been created SentioAI can also be used to run TAR.

SentioAI works both as a stand-alone product and is integrated with Relativity®. The software can run in the cloud or behind a client's firewall.

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Save time and money with SentioAI while increasing accuracy

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