How Natural Language Processing Helps Mattermark Find Business Opps – A Conversation with Samiur Rahman

Daniel Faggella

Daniel Faggella is the founder and CEO at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and many global enterprises, Daniel is a sought-after expert on the competitive strategy implications of AI for business and government leaders.

How Natural Language Processing Helps Mattermark Find Business Opps - A Conversation with Samiur Rahman 2

Episode Summary: Natural language processing (NLP) sounds cool in theory. We’re familiar with Siri and Echo of course, but where does it play a role in other companies? In today’s episode, we speak with Samiur Rahman from Mattermark, whose entire business model is predicated on organizing and making findable information about companies, and generating a platform to search by unique criterion. Doing so involves some conceptual work with NLP to make things findable. Samiur talks about what Mattermark is doing with this technology now and where he thinks the future may take the field, and interesting topic for investors and founders alike.

ExpertiseElectrical engineering and software development

Recognition in Brief: Samiur is the data engineer lead at Mattermark. His goal is to create the next-generation of intelligent products using modern machine learning and neural networks, and he’s particularly passionate about employing deep neural networks to tasks that were previously thought to be restricted to just humans. Previous to Mattermark, he held engineer positions at Synapse Product Development and Amazon. Samiur received his BS in Electrical Engineering from The Cooper Union for the Advancement of Science and Art.

Current Affiliations: Data engineering lead at Mattermark

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Interview Highlights:

(1:45) Give us some context as to how Mattermark is leveraging natural language processing (NLP) now?

(5:55) What informs the ‘show up or not’ process (of information) for NLP in the conceptual space?

(13:03) I’m interested in how you’re leveraging search at Mattermark now and where you see search broadly applicable in business.

(17:46) Do you think in the future that better search, a deeper kind of conceptual search will be able to emerge for things like Google docs, etc…do you think it will become more commercial, more broad scale like elastic search did to some degree?

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