Top 5 reasons partners are embedding IBM’s AI Insight Engine
By: Fredrik Tunvall
Tags: Unstructured Data, Business Intelligence, AI, Machine Learning, NLP

Top 5 reasons partners are embedding IBM’s AI Insight Engine

In spirit of it being the end of the year when lists are all the rage, this blog will explore the top five reasons business partners such as EY, EquBot, and LegalMation are embedding IBM’s AI-powered insight engine Watson Discovery to enhance their products and offerings. Fredrik Tunvall Dec 8, 2020·4 min read

As product managers we are always looking for ways to improve our products. We can build something completely new or enhance functionality we already offer. The third option which is becoming increasingly popular is embedding technology from someone else rather than building it ourselves. Below is a list the top five reasons why our partners are choosing to embed Watson Discovery technology into their products.

Ok, let’s get to it…

Cutting edge natural language processing (NLP) technology that can pinpoint answers in long and complex documents — One of the core capabilities of Watson Discovery is its ability to find relevant insights in long and complex documents based on a user’s query. With our newly released Reading Comprehension beta feature Watson Discovery is able to retrieve candidate paragraphs from a set of documents, search for the precise answer to the question at hand and return the best answers. Partners embed this technology for a multitude of reasons, but the most common is to provide more accurate search results in an application that manages documents (e.g. a M&A virtual data room) or to provide experts, like an field engineer or financial advisor, with exact information when they need it the most. Machine learning technology that reads and retrieves information from only relevant sections — Documents (often) contain a lot excess information that is not relevant to the question at hand. This abundance of information acts as noise that impacts the insights we receive. This is why partners value our Smart Document Understanding (SDU) feature. SDU is a visual machine learning tool that enables users to easily label and annotate different sections in a document, such as headers, tables, subtitles, images, etc. After only annotating a few pages, SDU will self-learn the visual structure of the rest of the document. This new visual representation of the document in turn helps Watson Discovery ensure that information retrieval only comes from relevant content, which means less noise and better insights. This feature has been extremely helpful for partners that work with long and complex documents, such as legal reports, technical manuals, government regulations, etc. Ability to customize NLP models to their domain, quickly — As maybe expected, Watson Discovery comes with out-of-the-box NLP enrichments including entity extraction, sentiment analysis, emotion analysis, keyword extractions, category classification, concept tagging and more. For partners that need something custom, we offer a NLP customization tool so partners can train Watson Discovery on the unique language of their specific use case, be it financial services, public sector or customer care. This means better enrichments and more relevant insights. Technology that reads and retrieves more than just text — Table identification and extraction is an increasingly important capability when it comes to insight engines. After all, many answers are found in tables rather than in a paragraph of text. Discovery has the ability to identify a table, extract relevant information then present it back either as text or as a table. This is extremely beneficial when working with documents that are table heavy, like annual reports, scientific material or product manuals. Developer friendly tools — Last, but not least, Watson Discovery offers a variety of tools to help partners utilize our many different capabilities and features. In addition to having UI tooling that makes it easy to ingest, enrich and query data we offer:

An easy to use REST API

SDKs for Android, Go, Java, Node.js, Python, Ruby, .NET, Swift, and Unity

for Android, Go, Java, Node.js, Python, Ruby, .NET, Swift, and Unity Rapid model development to build out a dictionary (or two) quickly.

to build out a dictionary (or two) quickly. Reusable React components to reuse the UI you build in Discovery across your applications.

And that’s the list. If you need ideas for how to utilize Watson Discovery for your applications, please take a look at some of the use cases below. If you have any further questions, please comment or visit our product page.

EY

EY embedded IBM Watson Discovery in its solution Diligence Edge to provide Merger and Acquisition professionals with an outside-in view of a target company and its competitors. EY Diligence Edge can consolidate and analyze a range of external information, including news, financial and social media data, to assist M&A practitioners in providing strategic recommendations at the click of a button.

EquBot

EquBot’s offers an AI-powered investment platform meant to help traders uncover new opportunities in the market. By embedding Watson Discovery they can extract information from unstructured data (such as annual reports, social media data, or company announcement) from publicly traded companies to provide unprecedented insights.

LegalMation

LegalMation developed a first-of-its-kind AI platform to automate routine litigation tasks. The solution embeds Watson Discovery to analyze complaints and create early-phase response drafts, LegalMation sees enormous potential to help legal organizations not only save time and drive down costs, but shift limited resources to focus on higher-value tasks, such as litigation strategy.