Finch Computing Working with Great-Circle Technologies
Finch for Text® Solution to Power Text Analytics Component of Entity Intelligence Platform
May 30, 2017
Reston, VA – Finch Computing, a division of Qbase, LLC, today announced today that it is working with Great-Circle Technologies, a unique provider of technology services and solutions, on a knowledge platform project aimed at delivering fast, accurate entity intelligence at scale.
Finch Computing’s Finch for Text® solution, a proprietary technology that works in real-time to make unstructured, human-generated text machine-readable, will be a key component of the knowledge platform.
“The needs of this project aligned perfectly with what we do well and differently than most,” Finch Computing VP of Sales Stacey Massey said. “We’re enthused about Great-Circle Technologies as a partner and about their approach to bringing together best-in-class technology providers in service of important missions.”
“What we found compelling about the Finch for Text® offering is its uniqueness,” said Great-Circle Technologies Vice President of Operations, Jerry Tiefenbrunn. “To be among the first to leverage its inventions in text analytics, and to bring those cutting-edge innovations to our customers, was really exciting to us. We’re looking forward to the partnership.”
Finch for Text® draws on an IP portfolio of two dozen patents covering things like topic modeling, compression and in-memory computing to perform four core text analytics functions: entity extraction, entity disambiguation, entity enrichment, and entity-level sentiment assignment.
It operates in real-time and at-scale, enabling the fast and accurate processing of huge, enterprise-size volumes of text. As an example, for just one customer, Finch for Text® handles an average load of 4 million streaming documents per day, with peaks as high as 9 million – all with a minimal cloud footprint.
Finch Computing built its own predictive analytics engine called FinchDB®, which enables Finch for Text®’s speed and scalability. At its core, it’s an in-memory, JSON-doc database that embeds predictive models in its queries. This enables entirely new ways of interacting with data – even as it changes.