Enabled a document management firm to mine data accurately and improve operations efficiency through automation

Extracting Valuable Metadata AI-powered Text Analytics for Document Management

Case Study: Extracting Valuable Metadata: AI-powered Text Analytics for Document Management

About the Client

The client is a world-leading contract and document management firm based in the US. Moreover, the client handles large volumes of contracts and documents for leading companies around the globe. Also, they help companies to retain and grow their base revenue with effective document management.

The Business Challenge

We built an IT Operations Analytics tool that will give the client access to accurate dashboards, predictive pricing, and predictive analytics.

What Aptus Data Labs Did

We built an AI-based LDA (Latent Dirichlet Allocation) model to mine metadata efficiently.

The Impact Aptus Data Labs Made

The LDA model implemented for text analytics has effectively extracted valuable metadata, resulting in an 85% accuracy and a 6% improvement in operational efficiency.

The Business and Technology Approach

Aptus Data Labs used the below process to build the text analytics model to resolve the existing challenge. 

Used text filtration to extract data based on titles, dates, contract party details, document category, and contact details
Used the following application to retrieve useful data:
  • Entity Extraction
  • Name Matching
  • Stop Word Removal
  • Stemming
  • Tokenization
  • Categorization
  • Topic Extraction
  • Transformation of Cases
  • Words to Vector
  • Sentence Extraction
  • Naïve Bayes Classification

Tools used

  • RapidMiner
  • ‘R’ Code integrated into Rapid Miner

The Outcome

The derived data helped the client to automate their process and increased their operations efficiency by 6%.

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