Recognant is an NLP Engine. What that means is that it takes a body of text and it adds meta-information to it. A lot of meta-information. Sometimes this information is boring like which words are nouns and which are verbs, other times it is more interesting like if a word has a positive or negative sentiment in most typical uses. This information is necessary for software to know if “Send a text to my mom we should buy milk” is a command to compose an MMS or to order Milk from Amazon.

The above is a very simplistic example. The engine also provides the index of documents such that you can find every place that the iPhone 6 was compared to the iPhone 5 by someone who writes with a high school level education.

Recognant is not the first product to do these tricks. SRI has been doing this with great success for almost 2 decades, and the majority of the code hasn’t changed in the last 10 years. But SRI leveraged Moore’s law to achieve these levels of success. The Government funded most of their development and when you are looking for terrorists the price per document indexed can be extraordinarily high. Because Recognant is built using modern programming techniques and optimized for speed, it is 3000 times faster than CoreNLP from SRI. This makes possible web-scale indexing, and makes many use cases that would be prohibitively expensive, or too slow for user interest to be possible.

Recognant also offers “Soft” metrics that are not included in competing products. The ability to detect comparisons, sarcasm, humor, alliteration, and other language devices makes Recognant much better for companies looking at psychographic information.

Because of the reduced CPU and Memory requirements, Recognant is embeddable. Recognant can be added to smartphones, cars, or IoT devices. SRI doesn’t offer a product that is equivalent, and as more and more devices look to have Natural Language Interfaces, and as privacy laws and concerns push more data off of the cloud to the local device this is an important feature.