This is a technology used for document review leveraging AI.
Based on a Human Reviewer’s document review results, AI learns
to determine relevance Yes/No, and organizes the documents
determined to have higher relevance to be prioritized for review.
Real-time AI Analytics
Accessible to Various Project
Out of the immense number of documents,
reviews documents determined to have higher
relevance and categorizes the other
documents to narrow the search.
Based on the insight of experts
received as input, AI conducts analysis
and separation of the remaining data.
Through real-time learning, AI analyzes
which materials are needed by the Human
Reviewers and prioritizes them according
Active Learning is a unique part of machine learning. It communicates with the Human Reviewer who provides input for the AI to learn, and proactively drives towards the point of decision-making. It mitigates uncertainties of the outcome and increases the accuracy of matters that are relevant. As a result, the outcome is delivered with speed and accuracy.
By leveraging Active Learning, documents can be analyzed in real-time,
and Human Reviewers can receive support on prioritizing documents of higher relevance for review.
This is a type of machine learning technique using Binary Classification to determine the range of a specific data set.
After learning the patterns between the data sets during training, decision-making is executed based on the support vectors of the two groups.
When there are ambiguous boundaries, the system takes a proactive approach to make a determination.