General, Product

AYLIEN Customer Case Study – 1043 Labs & Share Rocket

At AYLIEN, we provide the building blocks for our customers to create Natural Language Processing-powered solutions. To give you an idea of what some of these solutions look like, we occasionally put together use cases (check out how customers like Complex Media and Streem use our APIs).

For this blog, we’re going to show how 1043 Labs, a US-based software consultancy firm, used our Text Analysis API as part of an innovative platform they built for a client that recently closed a $5 million funding round to expand their operations.



1043 Labs is a custom software consulting firm that helps entrepreneurs get their ideas off the ground. They are a team made up experts from a wide variety of technology fields who bring their technical expertise to the table to help their customers build products, services, and solutions. So when Chris Kraft founded Share Rocket with a vision to create a ratings system for digital media, he hired 1043 Labs to help build the solution he had envisioned.

The Challenge

In the US, 2016 saw digital ad spending surpass that of television ads, with the global annual digital ad spend hitting $72.5 billion in 2016, a figure that’s set to continue to grow into the future. But to get a slice of this digital ad spend, publishers and media organizations across the board need to understand how their content performs so that they can make data-driven decisions on their strategies.




This is where Share Rocket can help. Using a collection of digital tools, Share Rocket’s customers like NBC, Fox, and Hearst can measure how successful their content is online. These tools make it easy to monitor social reach and understand how successful their content is online. Information and data obtained from Share Rocket help users to assess what type of content they need to produce more of. In turn, this allows them to make data-driven decisions on what content to produce and how to promote it, maximizing potential ad revenue.


Share Rocket


How does AYLIEN fit in?

To understand what content performs best, Share Rocket needs to understand what every piece of content is about. This allows their users to ask questions about the subject matter of their text content – what isn’t working, and what they should produce more of. Moreover, Share Rocket needs to understand this at scale. Their clients produce thousands of pieces of content per hour which means manually tagging all of this content would be almost impossible due to the scale alone.


AYLIEN 1043 Case Study


So when Share Rocket hired 1043 Labs to help build their platform, the consultants at 1043 Labs assessed a number of Natural Language Processing APIs with a clear idea of what exactly they were looking for and prioritized the following requirements:

  • Accuracy
  • Speed
  • Customer support  
  • Time to value

Following some in-depth testing and discussions with Mike and some of the engineering team, 1043 Labs chose the AYLIEN Text Analysis API.

“AYLIEN saved us about 3 person months of development. This made our client happier because we delivered the project earlier than planned and under budget.”

Mike Ostman, Founding Partner, 1043 Labs


We’ve heard Mike’s sentiment echoed in calls with our customers quite a bit. People respond really well to how easy our tools are to integrate into whatever you’re building – signing up takes a couple of minutes, and you’ll be making your first calls to the API with a couple of lines of code. After deciding to go with AYLIEN, the team at 1043 Labs had their entire solution built in a couple of weeks.

How are Share Rocket using the AYLIEN Text API?

Share Rocket’s users need accurate metrics on the performance of their content online, so to provide the full picture, Share Rocket need to understand what every piece of content is about. This allows their users to ask questions like ‘how do people like our sports coverage?’ Or ‘is our coverage of weather doing better than our competitor’s?’


Monetize - Share Rocket

But the problem here is that text content is unstructured, meaning it’s particularly difficult for computers to analyze and understand. This is where the Natural Language Processing comes in. Every time a new piece of content is published by their users, Share Rocket use the Classification endpoint of our Text Analysis API to understand what the content is about. Our Classification feature allows users to categorize content based on two industry standard taxonomies: IAB-QAG and IPTC. The ability to classify content automatically means Share Rocket can utilize assigned categories as tags in their proprietary SHARE and SEI tools. These tags can then be used to track content performance across subject categories.

Share Rocket now analyze upwards of 150,000 pieces of content every day for their users with our API. This is a testament not only to the fact that they’ve built a service people need, but also to the fact that our Text Analysis API is a robust tool that scales as your demand increases.

Getting started with our APIs is simple. Open your account by clicking on the CTA below. Once you’ve created your account you can start calling the API within minutes, with a few lines of code.

Text Analysis API - Sign up



Will Gannon

Content Marketing @ AYLIEN A Classics graduate from UCD, Will is on our Content Marketing Team here at AYLIEN. Before joining us, Will worked in research before completing a Master’s in Digital Humanities at Trinity College, where he used NLP methods to index where Latin terms appear in English Literature.