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Key features

The application ingests the desired contracts to be reviewed, creates and learns from semantic models, classifies clauses into categories guided by its SME-trained knowledge base, and determines the contract’s clause status based on context. Users can query contracts through a natural language interface application which could be a chatbot capable of identifying user’s intent speeding up the contract review process.

Machine Learning features

Natural Language Interface enabled query response

  • Users can configure themes based on queries
  • Users can review machine tagged data
Knowledge transfer

The architecture of system, is provisioned to integrate with pre-trained models in the domain, by process called transfer learning, leading to superior accuracy

Progressive learning

In addition, the network progressively learns (or self-learns) with human corrections or inputs

Language independence

The underlying representation of language in a numeric form called word embedding not only facilitates its computability, but also makes it independent of the natural language

Other Features

User’s intent identification

Semantic Analysis

Context based analysis

Extensibility to different types of contracts

Key benefits

Traditional processes for analyzing and managing contracts is resulting in loss of revenue, productivity, and increased risk. Nia Contracts Analysis changes it all

  • Improved Risk Management

  • Cost Reduction

  • Compliance assurance

  • Increased Productivity

  • Faster turn-around times from legal function to enable business better

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