iManage Extract
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Organizations are facing continuous growth in the rise of unstructured data. Emails, discussion threads, shared drives, collaboration sites – all generate tremendous unstructured content which is challenging to analyze, compare and translate into actionable data. This unstoppable growth is causing several problems for many professional services firms:
- labor costs to manually extract valuable data from content is extremely high, impacting project / matter profitability and firm competitiveness.
- manual processing time and offshoring of work to lower cost centers delay responsiveness and may impact client satisfaction.
- accuracy of human data extraction is inconsistent.
- team morale suffers when expensive professionals are pushed into doing this type of highly repetitive task.
iManage Extract uses Artificial Intelligence technology to automatically read, extract and interpret critical business information from large volumes of documents and unstructured data. With iManage Extract, you can increase organization productivity, reduce cost, save time and establish consistent and repeatable data and document review best practices.
- Transform project economics
- Quickly and accurately read, extract and analyze relevant information from large volumes of documents and data sets – saving 80-90% of manual costs. Turn money losing tasks into profit centers.
- Work smarter and faster and more accurately
- Get more work done in less time. Respond faster, improve satisfaction, quality and consistency. No need to hire additional resources for tight deadlines.
- Mitigate risks
- Reduce risks such as typos and other human errors caused by misrepresentation of data by different personnel.
Get more out of your organization’s data
By leveraging artificial intelligence to automatically read, interpret and extract key information, iManage Extract empowers organizations to get more out of their documents and unstructured data. iManage Extract automatically clusters large content sets and adds structure to unstructured data.
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