Automated Contract Analysis

Running multiple contracts and generate reports with associated risks and suggesting modifications to comply with Playbook.

Technical Features

Clause Classification:

Platform supports to find out the clauses of the contract document text.


Risk level identification:

Platform determines the risk level of the contract document text according to it’s determined clauses in 3 levels.

YELLOW RISK: The document text which has moderate risk.
RED RISK: The document texts which poses higher risk.
BLUE RISK: The document text with low risk and can be ignored.

Playbook Rule Fetcher:

A NLP trained model to take the risk sentences and find the root cause of it.

It also gives you multiple reasons for not complying with the playbook and a risk sentence.

TELCO NET Promoter Application

A unstructured data analytics platform helps improve the processing speed and accuracy of a telco’s NPS survey.

Executive Summary

Predictly’s unstructured data platform setup for processing 60,000+ verbatim including data cleansing and normalization.
Capable of instantaneous upload and analysis.
Saves 200+ man hours of manual coding and 50+ hours of text analysis.
Wide range of visualization charts for deeper insights.

Technical Features:

Used big data platforms along side Natural Language Processing to process vast amounts of data in a very short time.

Keyword Extraction and Sentiment analysis of the Survey outcomes.

Topic Modeling to group the words under topic buckets that may be relevant to take action.

Semantic indexing for quick search of text corpus.

Boolean search operators for exact search parameters.

Emotion analysis, a deeper analysis over generic sentiment study.

Filter enabled word cloud.

Association analysis to generate linkage between topics.

Content Analysis Tool

Enable users to process transcripts faster, more accurately and provide deeper insights for breaking the positioning clutter.

Executive Summary

Extract the semantic of the sentences in documents and build a semantic vector space.

Enables semi-auto meta tagging feature to override the meta tags predicted by the AI trained model.

Visual depiction of the entire uploaded text data and support for checking the initial hypothesis.

Making interaction with data easier and support to extract analysis in wide range of formats like excel sheet, csv files etc.

Technical Features:

Drilldown charts for aspect based sentiment analysis: A visual to show sentiments across different aspects with multiple drilldowns.

Word Cloud: A quick understanding of the uploaded documents context.

Topic Modelling to categorize responses with similar semantic.

Root-Cause analysis: A drill down visualization to determine the causality of a theme.

Advanced search filters for emotion, sentiments, opinion and topics.

A realtime Question answering feature to find out answers to your questions.

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