Nlg Company

NLG (Natural Language Generation) companies develop NLG software which helps companies auto-generate articles and reports. Most common use cases include:

  • Automated article generation speeding up content creation of media companies, helping them build long tail, timely content
  • Automated reports (e.g. financial reports) which enable executives to get to the facts without spending time with complex reporting software

To be categorized as an NLG company, a company's product must be capable of automatically producing text based on a variety of input including tables or other text

Innovators Specialists Leaders Challengers Market Presence Momentum
Popularity
Satisfaction
Maturity
Pricing
Country

Compare Nlg Companies
Results: 15

AIMultiple is data driven. Evaluate 15 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

Sort by:
93.15170685972518
95.04166666666667
0
100
34.72222222222222
77.7601779478501
top5 , top10
4star
Azure Text to Speech API
4.00
100%
100%
0%
= 2 reviews
= 30 employees
= 100,000 visitors

Improve user experience and accessibility for your apps by converting text to speech.

93.01264756351598
91.88610698748738
4.772730772404638
94.40736708969708
100
76.10166038238897
top10
top5 , top10
4star
IBM Watson Text to Speech
4.10
100%
100%
35%
= 2 reviews
= 30 employees
= 100,000 visitors

Convert written text into natural-sounding audio in a variety of languages and voices.

81.1705367850504
78.33217952325415
100
77.84831661541729
71.82539682539682
78.85180462141858
top5 , top10
top5 , top10
4star
Amazon Polly
4.30
100%
100%
100%
= 2 reviews
= 30 employees
= 100,000 visitors

Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled applications

74.18518946520666
67.9963929309274
29.54545364810138
71.39340125385874
0.007738095238095237
46.36574341575417
top5 , top10
top5 , top10
4star
Wordsmith
4.40
100%
35%
100%
= 2 reviews
= 30 employees
= 100,000 visitors

Natural Language Generation (NLG): Automated Insights is making the world's data understandable through natural language generation.

68.21967192177597
52.956104266011685
13.40908382000086
55.90759076897898
0.023214285714285715
61.38767438517648
top5 , top10
top5 , top10
4star
Readspeaker
4.40
58%
100%
100%
= 2 reviews
= 30 employees
= 100,000 visitors

ReadSpeaker provides lifelike online and offline text-to-speech solutions to make your products and services more engaging.

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, nlg company is less concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 77% (1% less than average) of search queries in this area.

Web Traffic

Nlg company is a less concentrated than average solution category in terms of web traffic. Top 3 companies receive 74% (3% less than average solution category) of the online visitors on nlg company company websites.

Satisfaction

Nlg company is less concentrated than average in terms of user reviews. Top 3 companies receive 57% (1% less than average solution category) of the reviews on nlg company company websites. Product satisfaction tends to be lower for more popular nlg company products. Average rating for top 3 products is 4.1 vs 4.3 for average nlg company product review.

Leaders Average Review Score Number of Reviews

Maturity

Number of Employees

Median number of employees that provide nlg company is 93 which is 30 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 13 companies (34 less than average solution category) with >10 employees are offering nlg company. Top 3 products are developed by companies with a total of 1-5M employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

IBM
Amazon Web Services (AWS)
Google
Microsoft

Learn More About Nlg Company

What is NLG?

Natural Language Generation (NLG) includes techniques for translating information into text. NLG is a subset of Natural Language Processing (NLP). If natural language processing techniques can be separated into two, Natural Language Understanding (NLU) is the part that analyzes natural language, NLG is the part that synthesizes meaningful text for human consumption. NLP=NLU+NLG. For example, let's imagine that you are writing to a conversational AI solution. When you ask what the weather is like today, NLU techniques are used first to understand your intent. Weather data is then searched, the results are analyzed and an answer generated thanks to NLG techniques.

NLG techniques can be used to automatically summarize and explain data in a human-like manner. NLG can automate the transfer process of analyzed structured data to humans in seconds.

How does NLG work?

An NLG engine works in 6 stages:

  • Content Determination: The limits of the content should be determined. The data often contains more information than necessary.
  • Data interpretation: The analyzed data is interpreted. Thanks to machine learning techniques, patterns can be recognized in the processed data.
  • Document planning: In this stage, the structures in the data are organized with the goal of creating a narrative structure and document plan.
  • Sentence Aggregation: It is also called microplanning and this process is about choosing the expressions and words in each sentence for the end-user.
  • Grammaticalization: this stage makes sure that the whole report follows correct grammatical form spelling and punctuation.
  • Language Implementation: Makes sure that the document is output in the right format and according to the preferences of the user.

Why is it important now?

  • Available data is increasing and NLG helps humans get quick insights from data easily.
  • NLG solutions help users get insights information from various data sources. Different data sources can feed complex information in a prioritized manner to NLG solutions and NLG solutions can communicate insights to users. A Gartner report in 2019 predicts that “By 2022, 25% of enterprises will use some form of natural language generation technology.” Though most industry analyst estimates are not accurate, they tend to be directionally right. We also agree that the market is set to expand as companies want to democratize access to their data among employees.

What are the NLG application areas?

Since NLG aims to make sense of the data and create human readable insights, it can be applied to all areas dealing with reporting, content creation and content personalization. Some of these areas are listed:

  • Banking & Finance: The banking industry highly relies on data and insights for performance reporting. Additionally, profit and loss reports can be automated using NLG systems. NLG techniques can be used to support fintech chatbots that interact with customers for personal financial management advice.
  • Manufacturing: As IoT devices are implemented more widely in production sites, they generate a significant volume of data useful for performance improvement and maintenance. NLG can automate communication of important findings so employees can take action faster.
  • Retail and wholesale: NLG solutions can provide not only product descriptions and categorization for online shopping and e-commerce but also help personalize customer communication via chatbots.
  • Media: Summarization and content creation can be aided by NLG solutions. Especially sports and financial news tend to follow similar templates and text explaining such events can be easily created.
  • Insurance: NLG solutions can help to improve communication of personalized plans for customers.
  • Transportation: Chatbots can deliver warnings about delays and schedules. NLG tools can be used to create personalized, easy to read travel plans.
  • Politics: Probably the most dangerous use case is using NLG solutions to spread personalized propaganda and misinformation. This is unfortunately has the risk of making the current flow of political disinformation even more dangerous and personalized.

What are the benefits of NLG?

NLG saves time and costs as well as contributes to scalability and consistency via automating repetitive processes involving language.  

  • Time and cost savings: NLG reduces time and costs as it speeds up the reporting processes.
  • Reporting consistency: Reports can be precise and standardized as though they are created by one person. Accuracy of the reports can be improved by time as NLG solutions advance.
  • Scalability: By automating parts of the reporting and content generation processes, it becomes possible to create large amounts of content.

How to choose NLG software?

Important factors in evaluating an NLG solution are: evaluating use cases, the existence of suitable data sources, security, language capabilities and vendor dependency.

  • While evaluating NLG solutions, it is necessary to find vendors that provide solutions for a particular use case. For example, an NLG solution trained to prepare content in the field of e-commerce would not be suitable for reporting the efficiency of IoT devices.
  • NLG solutions can be as good as the underlying data. The project team needs to ensure that they have the structured data which can be combined with the NLG solution to serve users.
  • The audience of the reports or content is important. Publishing public content is always risky and would require a more robust quality assurance process.
  • In situations where language skills are important, a robust assessment of the NLG tools' language capabilities is necessary. For example, correct use of language is far more important in media and journalism compared to a company's internal reporting.
  • Large-scale enterprises can build their own NLG engines using open source solutions and minimize vendor dependency. Two aspects are important in this decision: 1) Is the use case highly specialized? If yes, it may be harder to find an NLG solution from a vendor for that case. 2)Is this a simple or difficult NLG problem? Simple problems can be easily resolved with open source packages.