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Equals 3: The AI-Powered Assistant Every Marketer Needs

Posted by Equals 3 on May 29, 2018 4:57:00 PM

Lucy is the product of Equals 3, an AI startup founded in 2015 with $7 million in funding. She leverages the computing power of IBM’s Watson to research, segment, and plan for her human marketing counterparts.

We talked with Equals 3 managing partner Scott Litman (LinkedIn) and chief product officer Rahul Singhal (LinkedIn) to uncover all that Lucy can do.

In a single sentence or statement, describe Equals 3.

Equals 3 is the creator of Lucy, the AI-powered assistant to the marketing professional. Lucy addresses common challenges across research, audience persona modeling, and media planning allowing her human companion to tackle projects that require large amounts of data and get more done in dramatically less time.

How does Equals 3 use artificial intelligence (i.e. machine learning, natural language generation, natural language processing, deep learning, etc.)?

Equals3 uses a breadth of natural language processing technologies, machine learning algorithms, visual recognition technology, and natural language generation to build a proprietary engine that allows us to build a marketing knowledge graph to find answers from marketing data which can be structured or unstructured.

Most of the out-of-box technologies have been trained on openly available datasets (i.e. Wikipedia or other public sources). They tend to do a reasonably good job when asking questions that are not domain specific. However, the traditional NLP models fail to understand different concepts such as guerrilla marketing, paid advertising, brand awareness, and other common marketing concepts. We created our own domain-specific models to train Lucy to understand the vernacular of a marketer. Unlike most AI engines, we used a combination of deep learning models and domain-specific subject matter experts to help us create the knowledge. Understanding the intent of a question and which source is the best place to find the answer required our team to build an ontology of intents across structured and unstructured data sources. Now when a question is asked, Lucy has an intrinsic knowledge to understand the best place to find the answer and directs the question to be answered to the best possible repository. 
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