Information sourcing and research is a common problem for many companies out there.

As we know, research, information services, and financial services companies have specialized teams of research analysts that traverse, extract and summarize information looking for specific business value and correlations.

However, obtaining useful information from huge amounts of data require vast resources, both in terms of time and funds. The good news is there is the AI-based technology that is able to perform some parts automatically, and therefore improve the efficiency of the whole process.

A recent study shows, that 85% of executives will invest extensively in AI-related technologies over the next three years and in 5 years more than half of your customers will select your services based on your AI instead of your traditional brand.

The technology behind human-level text understanding

Let us introduce our patented technology for Named Entity Recognition, Information Extraction and Summarization.

It helps the analysts with most stages of information research.
It's able to turn thousands of materials into actionable reports - in a split of a second.
And it's easy to integrate into most workflows out there!

Case Study: Detecting Company Management Structure Changes

Let's focus on a particular, real-life example.
An investor needed information about changes in the management structure of companies in NYSE. Before using our solution, he employed a dedicated team of research analysts traversing thousands of web articles, tweets and social media posts looking for recent changes in the structure of the companies.

We're going to help their analysts automating their work on the way!

1. Collect the data from the online and local sources


The first step we need to do is to aggregate data from the valuable sources. So far, our client's analysts read online business newspapers and monitored selected Twitter accounts looking for structural change information.

Our system has entirely automated that work by scraping newspapers and tweets, producing a steady feed of information ready to use by the analysts, putting the information in one place.

2. Filter out the irrelevant materials


Well, you still need to go through all that text, don't you? Thankfully, not.

As the next stage in the pipeline, our state-of-the-art AI text classification algorithms precisely filter out all the irrelevant materials, basing their judgment on the context and actual meaning of the text.

3. Extract knowledge from text


We've already managed to save a lot of time by showing only the most valuable information.

However, there's still a lot of work to do. Working on raw documents is hard. We need to extract the information that the client needs.

And AI helps us in that as well!

Data scientists call this technique named entity extraction. It identifies important information to the investor in the text, based on given criteria.
In this case, we extract the company name, position, and the reason, and put it in a table row that we can use later.

At this point, it can be exported into JSON or CSV.


4. Generate insights and find correlations in the data


You probably know that the computer is great in processing tabular data, like Excel tables or databases, right? We call this type of information structured data.

The AI now further processes this data by:

  • prioritizing the most important structural change instances
  • filtering out based on certain criteria (e.g. companies outside a geographical region)
  • finding patterns and correlations in data
  • using the collected information to predict other changes in the market and stock movements (generate insights)

5. Generate a human-sounding, readable report


We're almost done!

Whew... that's a lot of work, hard to imagine it only took a fraction of a second.

Our investor fancies nice, readable reports that he/she can easily relate to and quickly find important information.

Thanks to another technology we've developed, called the Natural Language Generation (NLG), the artificial intelligence can generate human-sounding reports and summaries.

And - yep, you've guessed it - we're going to leverage this new machine skill to help our investor.

Our last step is to turn the raw table into an executive summary that the client can use to guide their further decisions.

Integrating all that goodness into the corporate workflow

We've made the NLP technology very accessible.

Depending on your needs, you can either:

  • plug it into your infrastructure using our microservice-based API
  • use it as a standalone cloud-based application, importing and exporting the data in popular formats, like Excel, CSV, or Word.

Reading list for the scientifically curious

The AI technology is very well described in various scientific literature. At Sigmoidal, our goal is to make the information more accessible. Have a look at the following article:

Natural Language Processing in Artificial Intelligence is almost human-level accurate. Worse yet, it gets smart!