Parse.ly

Parse.ly is a content recommendation engine. Online publishers use our dead-simple tools to integrate sophisticated personalization technology into their websites, creating new value from their existing content.

The Problem
There is no doubt news consumption is moving to the web.  25% of American adults read the news online daily -- that's up from 18% in 2006.  Most traditional media outlets realize the importance of being online, but they do not properly leverage the altered economics of online media consumption.  Instead, they have taken their old media format -- e.g. a newspaper subscription, printed to lowest common denominator audiences, with a "front page" and "sections" -- and simply ported that old model to the web.

This just doesn’t cut it. Though newspapers have over 36% of all U.S. Internet users visiting their sites, they are only able to grab 0.56% of the time users spend online. Is it any surprise though? Users still have to subscribe to sources and wade through pages of irrelevant content to find the information that resonates with them. With todayʼs technology content websites should be intelligent: they should understand a user's unique interests and learn from the user's behavior to recommend personally relevant articles. 

The Solution
Just as Pandora revolutionized the way users listen to music online, Parse.ly will revolutionize online news consumption.  Users tell Parse.ly what they care about, and our system matches their interest profile against thousands of online news and blog articles. Through our personalized reader, users can read highly targeted and relevant content that has been curated using their unique interests and their past reading behavior.  Thanks to collaborative filtering, the more users interact with Parse.ly, the better its algorithm gets.

Our private beta at http://parse.ly showcases our technology and allows us to gather feedback and data that serve to bootstrap our algorithmic approach to content recommendation.  It also earns us early press from the likes of TechCrunch and NPR.

Andrew, Technology Lead
Andrew is a technologist with nearly a decade of experience in software engineering.  He earned a bachelor's degree in Computer Science (with honors and departmental distinction) from NYU.  From 2006-2008, he acted as a technical lead on a small software team within Morgan Stanley.  During this time, he pioneered an open source initiative and presented his work at industry conferences, such as EclipseCon 2008 and Eclipse Banking Day NYC 2009.  Prior to his work there, he built web applications for a variety of clients, and was a published technical author and editor.

In the last several months, Andrew has acted as the technology lead on Parse.ly's development.  This involved total immersion in technologies for natural language processing and search, such as NLTK, Lucene and Solr.  Lately, he focuses on user experience, leveraging his deep background in Javascript and web UI design. He is happiest prototyping UIs for new features, testing them with real user feedback, and seeing what sticks.

Sachin, Business Lead
Sachin manages all operations for Parse.ly including: finances, logistics, hiring and business/customer development. He received a bachelor’s degree in Economics from NYU and a master’s degree in Education from Pace University.

He spent two years as a math/economics teacher for Brownsville Academy.  He spent nights and weekends integrating technology to improve classrooms, to solve the administration’s information management problems, and to provide new software tools for the school’s staff.  Due to the value of these services, he transitioned from teaching to become a technology consultant for several high schools.

At Parse.ly, Sachin has recently juggled roles in business development, investor relations, and customer relationship management.  He has also acted as the liaison between Andrew/Didier and our top users, assuming the product owner role to keep our feedback loop tight and fruitful.  

Parse.ly makes money via publisher tools and APIs.  We offer content producers a simple and effective way to integrate personalization and recommendation technology into their websites.  Our entry-level service is a "Personalize This!" button that publishers put on their sites to give users prioritized article recommendations within seconds.  This will dramatically increase account registrations and repeat visits.  For more demanding clients, we offer deep integration with our system via a Content Personalization API.

Content recommendation has proven value to users.  Pandora understood this -- that's why 20M people signed up for the service.  If media companies take Mr. Schmidt's advice, Parse.ly is a company they can contact today to increase user engagement in a matter of minutes.  They don't have to build their own Pandora; they can just license ours.

We will also give content sites the ability to monetize their users in a new way. Parse.ly isn't just solving the content recommendation problem.  It's also solving the ad recommendation problem.  To that end, we're building the world's largest user interest and reading behavior database.  Once we reach critical mass with our data, we will offer content providers a new revenue stream: personalized ads.

We have multiple competitive advantages.  Primarily, we already have a launched product in private beta with thousands of users signed up.  Our user interest database is growing, our product is iterating, and we are garnering interest from enterprise customers who want access to our API.  Moreover, we are a lean startup.  We will have significant revenue within the next couple of months, and within our first year we will become cash-flow positive.  Lastly, we are seeking a provisional patent on our InterestMatch technology, and will formally patent it by the year's end.