Menu Contact →

Natural Language Processing with Watson

IBM Watson
speaks the language of

your business
better than ever.


When it’s time to make a critical business decision, you want to access all the information.

Valuable information often exists
in forms of language that can be
hard for computers to understand:
PDFs. Charts. Tables. Call logs. Handwritten documents. Blog posts. News articles. Tweets.

Language is constantly evolving.
New idioms and industry-specific vernacular are born every day.

This is far too much data for a person to read, process and synthesize. But it is not too much for AI that can comprehend the language of your business.


With Natural Language Processing (NLP), disparate, unstructured data can be brought together and processed so you can understand what it all means and make more informed decisions.

in action

IBM Researchers are constantly working on frontier of linguistics and AI. So now, Watson can better comprehend human language, the language of your industry, and even jargon that’s specific to your company.

An energy company was struggling to retain institutional knowledge and built a knowledge base that could be accessed by Watson using NLP.

The results?



documents ingested, making actionable insights easily accessible1



reduction in employee time
spent researching1

USD 10 million


worth of time saved and employees kept safer1

What Watson
can do with NLP


What’s my data saying?

magnifier over data pieces

Digs through your data looking for hidden patterns, trends, and relationships between different pieces of content. A leading automaker used content mining to analyze over 1 million documents, like customer surveys and vehicle sensor data, in 10 minutes, resulting in saved costs.


Cite your sources.

chat questions and answers

When you ask a question, you get more than an answer. Sales representatives for a global materials wholesaler were struggling to respond quickly to customer queries on its sprawling product catalog of over 300,000 items. Passage retrieval allows the representatives to quickly look up relevant information, resulting in average training time being cut by half.

Smart document

Explore what’s relevant.

structures of a document

Understand the structure of your documents and evaluate sections that likely hold the most relevant answers and information. A large bank used smart document understanding to break down complex billing statements in order to generate more optimized pricing proposals. What took 10 days now takes two minutes, freeing up sales representatives for higher level tasks.


Organize it for me.

different data being organized

Groups lots of similar data from many places together for analysis. In a large retail customer service call center, agents can easily collect and cross-reference call logs that reference problems regarding a specific product issue, allowing them to both improve their customer service and feed higher quality information back to manufacturers.


How does it feel?

happy chat message and sad chat message

Interprets and classifies the response behind a piece of text, so you can know how people really feel. An airline call center can use sentiment analysis to determine whether a flyer is satisfied or upset, pinpoint the reason behind a given sentiment, and ascertain specific moments in an interaction where sentiment changed. This information can be used as an emotional bellwether, letting the airline know what customers at large are feeling about their flying experience.


What’s the takeaway?

person organizing data to make decisions

Reads huge amounts of information, across documents, identifies the most important bits, and produces a smart and concise paragraph. Investment banking firms acquire lots of data to drive decision-making. But financial analysts can’t read everything. Summarization allows these analysts to get just the top-level relevant news so they can make better investment decisions.


Give me the highlights.

highlights of documents

The next generation of Summarization comprehends data quality and relevancy, ranking bits of information and presenting key data points to you in order of importance. Financial analysts that are using summarization can also use keypoint analysis to rank financial data points from documents, news, and press releases, allowing them to spot higher-quality market signals amid the chatter.


1. Preserving institutional wisdom, IBM, August 2021

IBM’s NLP capabilities can be used individually, but they also play well together. They’re simple to integrate into your existing workflows and data infrastructure, and they can be used behind your firewall or on any cloud with safety and security.

Take a deeper dive into the technical capabilities. Watson Discovery scours your data, surfacing the most relevant insights with NLP.