What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in nlg software category. Our data sources in nlg software category include;
NLG (Natural Language Generation) companies develop NLG software which helps companies auto-generate articles and reports. Most common use cases include:
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
If you’d like to learn about the ecosystem consisting of NLG Software and others, feel free to check AIMultiple Conversational AI.
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We use the data sources on the side for ranking solutions and awarding badges in nlg software category. Our data sources in nlg software category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
AX Semantics
Quill
Wordsmith
Phrazor
Yseop
Taking into account the latest metrics outlined below, these are the current nlg software market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
AX Semantics
Phrazor
Wordsmith
Quill
Arria
These are the number of queries on search engines which include the brand name of the solution. Compared to other Conversational AI categories, NLG Software is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 80%, 4% more than the average of search queries in this area.
9 employees work for a typical company in this solution category which is 12 less than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 7 companies with >10 employees are offering nlg software. Top 3 products are developed by companies with a total of 291 employees. The largest company building nlg software is Arria NLG with more than 100 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
AX Semantics
Yseop
Quill
Wordsmith
Phrazor
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
This data is collected from customer reviews for all NLG Software companies. The most positive word describing NLG Software is “Easy to use” that is used in 5% of the reviews. The most negative one is “Time consuming” with which is used in 2.00% of all the NLG Software reviews.
According to customer reviews, most common company size for nlg software customers is 1-50 Employees. Customers with 1-50 Employees make up 48% of nlg software customers. For an average Conversational AI solution, customers with 1-50 Employees make up 49% of total customers.
These scores are the average scores collected from customer reviews for all NLG Software. NLG Software is most positively evaluated in terms of "Ease of Use" but falls behind in "Customer Service".
This category was searched on average for 185 times per month on search engines in 2022. This number has decreased to 110 in 2023. If we compare with other conversational ai solutions, a typical solution was searched 814 times in 2022 and this decreased to 720 in 2023.
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.
NLG saves time and costs as well as contributes to scalability and consistency via automating repetitive processes involving language.
Important factors in evaluating an NLG solution are: evaluating use cases, the existence of suitable data sources, security, language capabilities and vendor dependency.