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Nihilent Technologies Delivers New Generation Abstraction Platform

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GNet Group, a subsidiary of Nihilent Technologies, is proud to announce the recent launch of its next-generation semantic intelligence engine for data mining of key data from large numbers of legal documents. This Abstraction Platform is the result of a partnership between Nihilent Technologies and Brightleaf Solutions, Inc and adds an important dimension of using unstructured data to GNet Group’s existing BI capabilities.

“This joint effort has yielded new thinking about an old problem: how do you get meaningful data from unstructured, English language legal documents and ensure the results are accurate and reliable. Today’s delivery of v1.0 of our new abstraction platform is the result of that.” Stated Samir Bhatia, CEO of Brightleaf Solutions.

Abhay Ghate, Chief Technology Officer for Nihilent Technologies said “We have a lot of experience with language parsing and using it to build structured databases. Brightleaf showed us how to apply that to legal documents which use precise language and are often lengthy and complex. It’s quite different than extracting data from written forms or other textural documents such as corporate reports, books, or news stories.”

Bhatia continued “Brightleaf’s new semantic intelligence engine is rule-based so we can modify and tune it for specific document types, thus gaining the quality and consistency that only comes with automation. Combining this technology with an extensive people-based quality control process yields results that meet the most stringent quality requirements.”

Looking to the future, Nihilent and GNet Group continue to innovate to bridge the gap between our client’s business requirements and the latest technologies.

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Brightleaf’s Automated Contract Abstraction Solution

 
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We’ve done it again! GNet Group has been named 100 Best Companies to Work For

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For the 2nd year in a row, GNet Group has been named Minnesota Business Magazine’s 100 Best Companies to Work For. The 2015 “100 Best” were selected by an independent research firm employing an anonymous online questionnaire filled out by the employees of each company — to determine which companies in Minnesota excel in the areas of work environment, employee benefits, and overall employee happiness, making them the 100 Best Companies to Work For.

Minnesota Business Magazine's 2015 100 Best Companies to Work For

 
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Predict Breast Cancer Biopsies for Malignancy Using Azure Machine Learning

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The video embedded above, which demos the use of Azure ML with Excel 2013 to predict malignancy of breast cancer biopsies, contains references to the following links: Read more…

 
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Azure ML Minnesota Training Event Follow-Up

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I am publishing this blog post as a follow-up to my Azure ML training session on December 10 at the Microsoft Experience Center in Edina. We appreciate the attendance and feedback from all of you who attended the session. Our marketing team has sent out a follow-up email with the slide deck and the links which were referenced in the presentation. If you have any additional feedback or questions about Azure ML or the presentation, please feel free to reach out and contact me.  My contact information is as follows:

Twitter: @GRBeaumont

LinkedIn: https://www.linkedin.com/in/gregbeaumont

Following are some links that were referenced during the presentation, and which should be helpful for those of you who are looking to learn Azure ML:

Microsoft Links

Data Links Read more…

 
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Managing Deliverable Expectations for Business Intelligence Project Success

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Business Intelligence (BI) projects are usually intended to meet a hybrid of different needs, frequently including standardized reports and dashboards, self-service reporting, and data discovery tools. Quite often, unexpected relationships and architectural challenges are found in data during the course of a project. Experienced BI teams are not strangers to mid-project discoveries such as many-to-many relationships instead of traditional dimensions, key relationships that do not map properly, and calculations that need to be modified for the unique needs of the business users.

Despite detailed working sessions with business users and preliminary data analysis, sometimes changes to requirements are needed for a project to meet the needs of the business. Even the most carefully planned projects can end up with initial SOWs that don’t fully capture the details of the actual development required to complete a project. Read more…

 
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