Artificial Intelligence as a Service

sasha uritsky

Mar 11, 2017

There is a growing demand in the industry for Artificial Intelligence products, from simple chatbots to conversational ecommerce solutions to advanced intelligent systems.

And there is a growing number of AI companies offering such products.

One of the problems however is that AI products currently available on the market require technical sophistication on behalf of the user, such as familiarity with APIs, communication protocols, XML, etc.

nmodes aims to solve this problem. Our position is that the users do not need to be technically savvy to enjoy AI capabilities. We offer our AI solutions as a service, fully hosted, fully supported.

We do not ask for any technical knowledge from our customers. We only want them to tell us the details relevant to the business process they are looking to implement or support and we will take care of the rest.

In particular

1. We train AI to understand and support their own use cases.

2. We host the entire solution, without claiming the ownership of the data we process or use to train our AI.

3. We support all user interfaces ( UI ) required by our customers.

4. We connect to third-party APIs and integrate our AI with third-party components.

Artificial Intelligence as a Service ( AIasS ) that we offer makes new AI technology easier to use increasing its exposure to businesses and organizations worldwide.

Interested in reading more? Check out our other blogs:

Social Marketing is Simple

In its very essence social marketing is based on one simple foundation - give first, take later.

This concept of giving to the community is hardly possible to overestimate. It defines the way social networks operate and goes even deeper, to the basic principles of social interactions among humans.

In fact it is a much healthier foundation for business than traditional one, based on advertising.

Yet it runs contrary to what many entrepreneurs and business people perceive as a proper marketing approach.

Traditional marketing, such as billboards, radio ads, posters, banners, emails blasts, etc is based on two principles, a) the statistical law of big numbers, aiming to reach out to as large audience as possible while knowing that only a small percent would become interested, b) message of self-promotion and self-advertisment.

Social marketing negates both of these principles.

Social marketing is personal, it operates individually, and in a personalised way. Which makes perfect sense from a common perspective. Would you rather be bombarded by the generic ads that in most cases have nothing to do with your interests and desires, or approached on a one-on-one basis with a chance to discuss your specific needs?

Social marketing is directed towards promoting the interests of others, not yours (or your business). Again it makes sense as we are a social species, we live in societies and rely on communication. The most successful communication strategy is the one that takes care of the needs of your communication partner.

And so, opposing the traditional marketing approach, social marketing is based on the idea of giving to the community. Which makes it more efficient than traditional marketing, if measured against the effort applied. In other words, taken 100 random prospects, we are more likely to convert them into customers if using social marketing than traditional marketing.

But is it scalable?

(to be continued)

Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)