Content
mining

What’s my data saying?

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.

Passage
retrieval

Cite your sources.

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
understanding

Explore what’s relevant.

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.

Topic
clustering

Organize it for me.

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.

Sentiment
analysis

How does it feel?

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.

Summarization

What’s the takeaway?

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.

Keypoint
analysis

Give me the highlights.

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.