Amazon Web Services Case Studies Using Machine Learning on AWS to Eliminate Manual Contract Reviews

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	Using Machine Learning on AWS to Eliminate Manual Contract Reviews Amazon Web Services

Using Machine Learning on AWS to Eliminate Manual Contract Reviews

Amazon Web Services

Technology Category	Analytics & Modeling - Machine Learning Analytics & Modeling - Natural Language Processing (NLP)
Applicable Industries	Software Professional Service
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Computer Vision
Services	Data Science Services Cloud Planning, Design & Implementation Services
Challenge	Companies experiencing rapid growth often lack the bandwidth to track each line of every contract, service agreement, or legal document before it’s executed. Even in the most carefully reviewed agreements, some information is forgotten as soon as the contract is signed. Once the business has matured and due diligence projects arise (for example, when a law changes or an acquisition takes place), companies must conduct detailed reviews of all signed contracts and identify specific terms within them. LinkSquares’ founders experienced the painful reality of reviewing existing legal contracts firsthand while their previous employer underwent an acquisition. The team identified existing software solutions helping companies efficiently address the pre-signature workflow: contract creation, terms negotiation, and internal workflow. However, the industry lacked a software solution to help companies mine for information in existing contracts. Read More
About Customer	LinkSquares is an industry disruptor in contract review, providing high-growth companies with a suite of tools to complete fast and systematic legal reviews of executed business agreements. The company was founded by individuals who experienced the painful reality of reviewing existing legal contracts firsthand while their previous employer underwent an acquisition. They identified a gap in the market for a software solution to help companies mine for information in existing contracts, and thus, LinkSquares was born. The company is focused on post-signature contract analysis and does not deal with anything pre-signature. They built their software as a service (SaaS) offering on AWS to quickly migrate companies from their existing storage solutions and enable them to understand what they agreed to in their contracts. Read More
Solution	LinkSquares turned to SFL Scientific, a data science consulting firm, AWS Partner Network (APN) Consulting Partner and AWS Machine Learning Competency Partner, to build a scalable solution for identifying and classifying legal language. SFL Scientific used Natural Language Processing (NLP), an Artificial Intelligence (AI) method helping computers understand and interpret human language, to build its machine learning algorithm. Implementing the algorithm enabled LinkSquares’ software to extract key terms from a document and tokenize these terms into predefined categories. Upon deployment on AWS, the algorithm ran the code on demand. Whenever a document was uploaded, the machine learning code automatically launched. The NLP algorithm developed by SFL completely revolutionized the post-signature contract review process for LinkSquares. The machine learning code enables the LinkSquares software platform to automatically run code on thousands of documents in seconds. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Natural Language Processing (NLP)

Applicable Industries

Software

Professional Service

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Computer Vision

Services

Data Science Services

Cloud Planning, Design & Implementation Services

Challenge

Companies experiencing rapid growth often lack the bandwidth to track each line of every contract, service agreement, or legal document before it’s executed. Even in the most carefully reviewed agreements, some information is forgotten as soon as the contract is signed. Once the business has matured and due diligence projects arise (for example, when a law changes or an acquisition takes place), companies must conduct detailed reviews of all signed contracts and identify specific terms within them. LinkSquares’ founders experienced the painful reality of reviewing existing legal contracts firsthand while their previous employer underwent an acquisition. The team identified existing software solutions helping companies efficiently address the pre-signature workflow: contract creation, terms negotiation, and internal workflow. However, the industry lacked a software solution to help companies mine for information in existing contracts.

About Customer

LinkSquares is an industry disruptor in contract review, providing high-growth companies with a suite of tools to complete fast and systematic legal reviews of executed business agreements. The company was founded by individuals who experienced the painful reality of reviewing existing legal contracts firsthand while their previous employer underwent an acquisition. They identified a gap in the market for a software solution to help companies mine for information in existing contracts, and thus, LinkSquares was born. The company is focused on post-signature contract analysis and does not deal with anything pre-signature. They built their software as a service (SaaS) offering on AWS to quickly migrate companies from their existing storage solutions and enable them to understand what they agreed to in their contracts.

Solution

LinkSquares turned to SFL Scientific, a data science consulting firm, AWS Partner Network (APN) Consulting Partner and AWS Machine Learning Competency Partner, to build a scalable solution for identifying and classifying legal language. SFL Scientific used Natural Language Processing (NLP), an Artificial Intelligence (AI) method helping computers understand and interpret human language, to build its machine learning algorithm. Implementing the algorithm enabled LinkSquares’ software to extract key terms from a document and tokenize these terms into predefined categories. Upon deployment on AWS, the algorithm ran the code on demand. Whenever a document was uploaded, the machine learning code automatically launched. The NLP algorithm developed by SFL completely revolutionized the post-signature contract review process for LinkSquares. The machine learning code enables the LinkSquares software platform to automatically run code on thousands of documents in seconds.

Impact #1	LinkSquares was able to focus its resources on optimizing products rather than maintaining infrastructure.
Impact #2	The company was able to scale and improve accuracy with the help of the machine learning solution developed by SFL Scientific.
Impact #3	The solution provided by SFL Scientific completely revolutionized the post-signature contract review process for LinkSquares.

Impact #1

LinkSquares was able to focus its resources on optimizing products rather than maintaining infrastructure.

Impact #2

The company was able to scale and improve accuracy with the help of the machine learning solution developed by SFL Scientific.

Impact #3

The solution provided by SFL Scientific completely revolutionized the post-signature contract review process for LinkSquares.

Benefit #1	The machine learning code enables the LinkSquares software platform to automatically run code on thousands of documents in seconds.
Benefit #2	Every result showed an exponential improvement in time spent reviewing each document.
Benefit #3	Improvement in tagging accuracy compared to the human auditors.

Benefit #1

The machine learning code enables the LinkSquares software platform to automatically run code on thousands of documents in seconds.

Benefit #2

Every result showed an exponential improvement in time spent reviewing each document.

Benefit #3

Improvement in tagging accuracy compared to the human auditors.

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Overview

Using Machine Learning on AWS to Eliminate Manual Contract Reviews

Operational Impact

Quantitative Benefit