Resources

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Inside Story Engine™

Using Emotional Intelligence, Behavioral Analysis and AI Model Library, Story Engine allows firms to leverage work from previous cases and apply that knowledge to new cases. This allows teams to build out their own library with AI models to win new clients and extend existing client relationships without new investments.


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White Paper: AI Models

What is an AI model? What value does this technology bring to law firms? How has the AI market evolved — and how is it projected to grow? Our team took a deep dive into these questions to share how AI investments are skyrocketing, what’s driving that growth and what this means for the future of legal services.

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Use Case: Morgan Lewis

Morgan, Lewis & Bockius LLP licensed NexLP’s Story Engine to further its capacity through artificial intelligence to its clients. Following a successful beta, Morgan Lewis employed NexLP’s software to  better interpret language and related data to streamline its eDiscovery practice and boost client offerings.

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Story Engine™ Case Study

NexLP uses Machine Learning and AI to mine data for patterns and anomalies, map custodian relationships and conversations, and documents in a fraction of the time other applications require. Learn how one firm benefited from our cognitive analytics tool and saved $775,000 in review fees through use of Story Engine.

Explore the NexLP Platform

Inside Story Engine


NexLP FAQs

What is NexLP?

NexLP is a Chicago-based artificial intelligence software company that uses advanced artificial intelligence and machine learning to augment humans’ ability to derive actionable insight from unstructured data. NexLP’s Story Engine™ harnesses AI and machine learning to derive actionable insights from structured and unstructured data to drive operational efficiencies and deliver proactive risk mitigation for legal and compliance teams.

NexLP stands for Next Generation Language Processing (NLP). Leveraging the latest advances in Natural Language Processing (NLP), Cognitive Analytics, and Machine Learning, NexLP is a next-generation machine learning and natural language processing software platform. NexLP’s platform, Story Engine, turns disparate, unstructured data - including email communications, business chat messages, contracts and legal documents - into meaningful insight that can be used to act, as well as combined with structured data to create a truly comprehensive view of the entire data universe.

What is Natural Language Processing (NLP)

NLP is a type of artificial intelligence that utilizes computers to analyze and understand what humans are communicating about. Consumer applications like iPhone Siri and Amazon Alexa utilize NLP technologies as well. Natural language processing is a type of artificial intelligence that allows computers to better interpret and process human language.

What are AI Models?

An AI model is an algorithm that generates a score for an object. Models act as a container of features. Feature represent the collection of past knowledge the model has learned. An AI model is formed similarly to how a person’s mind develops. Learned behavior is based on patterns. By capturing and studying the expertise and experience of a past occurrence while attempting to containerize that knowledge, a specific scenario can be re-created.

How Do AI Models Streamline Business Practices?

AI Models take the collective consciousness of past matters and instantly apply learned knowledge to all incoming matters. An AI model library acts as a resource for firms to select the appropriate model for similar cases to be used today and in the future. This allows firms to analyze documents and data with contextual review far faster than a human’s mind could compute.

AI models form from autonomously learned behavior, which is also based on patterns, By capturing and studying the expertise and experience of a past occurrence firms can recreate specific scenarios and find information within data that they didn’t even know they were looking for. 

What is Authentic AI? 

Authentic AI is much more than just text analytics. NexLP’s Story Engine leverages predictive coding, combined with next-generation Natural Language Processing, cognitive analytics and dataless classification to turn disparate, unstructured data into meaningful insight that can save time, money, and unnecessary risk.

The difference in an authentic AI approach is the application of cognitive analytics. By independently classifying millions of documents full of unstructured data ahead of a review team, organizations can quickly and efficiently identify risk factors.  For example, leveraging NexLP’s Story Engine — which uses AI and machine learning to derive actionable insight from structured and unstructured data — enterprises can learn where potential risk exists.


What is COSMIC? 

COSMIC stands for Cognitive Machine Computing and is NexLP’s active learning TAR 10.x workflow. COSMIC’s artificial intelligence enrichment process goes beyond the Four Corners of the document to combine high performance with an easy to use active learning workflow. Project management overhead is kept to a minimum through auto-batching, granular coding, and rich statistical reports. On average, clients only need to code 2000 documents for the Model to reach “stabilization.” Stabilization is the process for which the AI computes that it is no longer “learning” from the coding/training process. Defensibility of the process is built-in with COSMIC’s unique Incremental Control Set feature and feedback is provided with an easy to read Stability measurement. 

What is COSMIC Model Library? 

A COSMIC Model Library provides an automated way to turn knowledge and work product into encrypted artificial intelligence (AI) model. Your AI model can be dropped into other matters for automated intelligent classification. You can further refine the classification with COSMIC’s active learning workflow. Developed by the industry’s top AI data scientists, Story Engine™ COSMIC Model Library allows you to automatically develop and brand your own AI knowledge repository. AI models for issues, relevancy and even privilege can be developed and easily deployed across matters. This means a customized AI experience, faster intelligence of document collection, and re-use of previous review investment

What are the Benefits of Predictive Analytics Technology 

AI technology allows for a better review of documents and relevant data, this enhances how quickly firms can analyze materials to discover both patterns and inconsistent language. The ability to augment this review process is where true savings can be realized — both from a monetary and time-saving perspective.

Leveraging the power of pattern recognition, emotional intelligence, behavioral analysis and natural language processing, AI predictive analytics can extract data that leads to better decision making. AI technology decreases the chances for errors, and equips firms with the right — and better — information to strengthen cases through more compelling evidence, all in a shorter period of time. 

What is the Value of the Predictive Analytics Market Today?

The predictive analytics software market is propelling the efforts of enterprises and law firms thanks to the anticipated increased customer demand and productivity gains from automating processes. The market is projected to reach $6.5 billion in 2019 — up $4.5B from 2012. Artificial Intelligence, in general, is projected to be a $15.7 trillion market by 2030, according to PwC data — with the U.S. accounting for $3.7 trillion of that growth. 

How Does Predictive Analytics Enhances Data Review

Instead of lawyers and investigators finding and labeling documents through manual methods, AI technology has taken the legwork out of the equation. Instead, firms are able to leverage AI tech to holistically analyze data with contextual review at scale with better speed and accuracy. The ability to detect patterns, flag risk and discover information relevant to a specific case in a matter of minutes — as opposed to weeks or months — is where the real value of AI and predictive analytics can be realized.

Through the implementation of enhanced data analytics, legal teams are equipped to mine massive data sets in little time to bring about better evidence analysis. They are also prepared to eliminate costly and ineffective manual review processes. 

How Does AI Technology Help Mitigate Corporate Risk?

AI is the perfect compliment to human judgement when it comes to mitigating risk. By relying on computer signals to detect risks that exist within unstructured data (emails, texts, communication, etc.), organizational teams are able to better detect risks faster than a human would likely discover on its own. Computer algorithms rely on AI models to ensure risks are continually caught faster. Models can detect risk signals at the speed of tens of thousands of employees. Computer algorithms, unlike humans, interpret data based on patterns and do not rely on a team of people with varying levels of information to make biased decisions, often on incomplete information. 

What is the Value of Deep Learning Technology for AI?

Deep learning, a component of AI and machine learning, allows for knowledge to be gained using artificial neural networks that are modeled on how a human’s biological nervous system operates. While this concept has been around for decades, recent technological advances have allowed software to compute data at rapid rates in order to gain a comprehensive overview more effectively. As a result,  massive amounts of data that power neural networks can be analyzed faster than ever before.

Deep Learning technology involves a powerful set of techniques for creating neural networks — which are created to mirror how the human brain thinks and operates. Leveraging AI deep learning allows for organizations to make sense of unstructured data in order to derive actionable insight to learn from the datasets. This is also commonly referred to as deep neural network.

How Is Deep Learning Used to Enhance AI Applications?

Deep learning is used to create predictive data models that can be used to understand data, and leverage that data to create actionable insights. By tapping into the power of machine-assisted descriptive, predictive and prescriptive analytics, deep learning allows for more sophisticated decision making based on the analysis of patterns that exist in the text and images within the necessary datasets. Deep learning renders decision making more accurate because of the automated data aggregation and document review process. Instead of focusing on manually processing and reviewing data, organizations are better equipped to proactively understand what exists within their own data when utilizing deep learning.

How is NexLP Different in the Artificial Intelligence Software Market?

Powered by the work of NexLP Co-Founder Dr. Dan Roth, the winner of the 2017 John McCarthy Award — given to the most distinguished group of AI experts in the world — Story Engine is based on Authentic AI that understands the context within the data universe.

Our platform goes beyond text analytics by focusing on the context around the data, not just what the words are saying. NexLP’s Story Engine analyzes and autonomously classify millions of documents full of unstructured data ahead of a review team.

Story Engine can immediately show sentiment analysis. This analysis can identify documents with negative sentiment, high-pressure content (where the receiver feels like they are under pressure), opportunity score and rationalization (where someone is rationalizing behavior).

Story Engine can also show over 60 key features of data like geographical places mentioned, instances where money was discussed, the top communicators in the data set and even perform social network analysis. Our platform encompasses two products. Story Engine, which searches, analyzes, and investigates complex datasets to tell a story; Story Engine I³ actively monitors enterprise communications to turn disparate data into decisive actions.

What Technologies Does Story Engine Use? 

Story Engine’s technology was designed by some of the top AI scientists in the field. Select examples of technology and capabilities utilized in Story Engine include:  Deep Learning o Neural Networks NLP (natural language processing) Logistical Regression Named Entity Recognition Name Disambiguation and Resolution Network Analysis  Sentiment Analysis Data Anomaly Detection “Patterns” Dataless Classification

How does Story Engine™ differ from other eDiscovery tools? 

Many tools ask the end-user to find the smoking gun documents or relevant documents on their own. Using state of the art artificial intelligence and visualization, NexLP’s cognitive computing platform provides clear recommendations to the end-users where they should be reviewing for unparalleled speed and results. The Story Engine solves the Cold-Start problem endemic to eDiscovery and Investigations. A Cold-Start is where to begin with a mountain of data. 

Does Story Engine work with third-party applications? 

As a part of the Relativity®’s Ecosystem, Story Engine™ is designed to integrate with Relativity® and utilizes Relativity’s APIs. Clients have also connected Story Engine to other systems like IPRO. In addition, Story Engine I3 integrates with data sources like MS Exchange, Office 365, Gmail for Business or native files. 

What Common Industry Problems Does NexLP’s Platform Address?

Story Engine is not about processing, and it’s not just about review. Our platform addresses both sides of the data analysis equation. We help firms and enterprises understand the urgency and value of implementing AI as an early adopter (specifically, people in task-oriented, cost center roles). Our platform improves organizational performance and reduces spend by analyzing multiple data channels for a 360-degree view of business data to find additional opportunities. With Story Engine, anyone can build their own AI Models that can be deployed for nearly any unstructured data set. An AI model library allows firms to apply previous case knowledge to new cases to continually make their own AI models smarter and faster.

What Pain Points Our Clients Are Trying to Address?

There’s a common theme among everyone we talk to: Sorting through data manually is a time-consuming and costly task. With a need to leverage actionable information faster, many firms want to replace existing technology that will solve their current data analysis needs. Instead of expanding technology budgets to include a new item, many firms express the need to replace a current system by adding on additional expenses to their team.

Potential clients often don’t even realize they have a problem until we explain how NexLP’s AI platform would transform their current workflow. From there, it’s easy to recognize how our technology can function within their existing tools. Story Engine helps legal teams find the facts that matter most, faster by eliminating the need for complex queries. Our platform is backed by AI specifically designed for investigation and legal teams.

What Apprehensions do We Hear the Most About Using Machine Learning/AI Technology?

Many don’t feel they understand the technology enough to be confident in their ability to use the platform/invest in it. It is a common fear among firms that relying on technology will cause them to overlook key evidence.They also often question how technology will be perceived by their clients, and if will make them look less valuable to their own firm. Comprehensive document review, however, is a key component of our AI technology.

How Does the NexLP Platform Address Apprehension About Machine Learning/AI Technology?

You don’t have to be an AI expert to leverage our technology. We make it quick and easy to integrate our platform into your existing processes so you don’t have to reinvent how you’re conducting data analysis. Our approach simply manages it better, faster and more effectively.

Using our break-through tools like Emotional Intelligence, Behavioral Analysis and AI Model Library, Story Engine™ allows firms to leverage all the work they’ve done on previous cases and apply that knowledge to new cases, which makes their AI models continually smarter and faster. With little effort, this allows teams to build out their own library of AI models to win new clients and extend existing client relationships without new investments.

What Does the NexLP Onboarding Process Look Like?

We provide a client on-boarding experience that is unsurpassed in the legal market. NexLP was built by legal tech industry veterans who understand the unique challenges faced by legal customers. Our client success team guides new clients through a detailed onboarding process that has been proven successful with hundreds of installations worldwide. Whether the installation involves our cloud service or an on premise installation, or whether we’re providing admin level training or end user onboarding, the NexLP customer success team ensures an enjoyable experience.  Then, once our AI technology is in place and the users are ready to access Story Engine, all apprehensions melt away seeing the incredible efficiencies play out. Our goal is always to show demonstrable efficiencies and these reveal themselves within days of using Story Engine.

How Can Organizations Use AI to Identify Risk?

Partnering with the right AI player is the first core component for managing and tackling risk that exists in and outside an organization. For example, working with an AI platform that targets incidents at the source — instead of after the fact — gives enterprise teams time to resolve issues before they escalate. That is a core tenet of NexLP’s AI platform. NexLP’s AI platform alerts teams to compliance, litigation and human resource issues as they arise. This is done by targeting issues at the source.