Luminoso Debuts Express Text Analytics System

Boston, MA-based text analysis firm Luminoso has launched Daylight Express, an application offering rapid analysis of unstructured text in surveys, product reviews, call center transcripts and other sources.

MIT spin-off Luminoso combines Natural Language Understanding technology with a 'vast' knowledge base to learn words from context, in a similar way to humans. Daylight Express is the first text analytics solution it has launched since closing a $10m Series B funding round in December 2018, and will allow Luminoso to start selling to a much broader global market, according to the firm.

The new tool uses the company's proprietary QuickLearn technology and promises rapid set-up requiring no technical knowledge, a t a number of price points depending on use volumes. Users upload customer feedback datasets related to a specific product, service, or brand, and get immediate answers to questions such as 'What issues are affecting the star rating of a company's product or service?' or 'Which concepts in the feedback are most prevalent?' the firm claims Daylight Express 'dynamically understands newly-uploaded text-based data, even with a constantly evolving vocabulary' and can 'analyze text for insights with less than 1/1000 of the data required by alternatives'. Fifteen native language models are available, including Arabic, Russian, nine European and four Asian tongues.

Chief Product Offices Ying Chen (pictured) comments: 'A few years ago, we launched Luminoso Daylight to offer companies the fastest way to uncover business-critical insights from their customer feedback datasets - an alternative to the current time, effort, annd training data intensive approaches. Through our work on large-scale implementations of Luminoso Daylight with dozens of the world's most well-known brands, we saw an opportunity to help organizations that have a different scale of text analysis needs, or those looking to augment their existing solution to see more immediate value when analyzing feedback data'.

Web site: www.luminoso.com .

All articles 2006-23 written and edited by Mel Crowther and/or Nick Thomas, 2024- by Nick Thomas, unless otherwise stated.