Others Talk, We Listen®

An in-depth look at early adopters and consumer drivers with the growth of Natural Language Processing

On May 9th, Google announced ML Kit for Firebase. Aimed at ordinary developers, rather than data scientists, this release brings machine learning functionality direct to both iOS and Android devices. Six features headline the release: * Facial recognition * Barcode recognition * Image labeling * Landmark recognition * Text recognition * Custom Models Like Apple's CoreML, Google engineered ML Kit to run machine learning models on a device, not to build those models.

I often hear the terms customer experience (CX) and user experience (UX) used interchangeably; it’s an understandable oversight, but one that has the potential to cause confusion and send marketers down the wrong path as they seek to advance the cause of their brands through improved experience.

Fully understanding the customer and leveraging new tools and methodologies to quickly implement a sound strategy

What are “hybrid” and “blended” agile methodologies, and what does it mean when people talk about “by-the-book” agile?

Few large businesses today can afford to build and deliver data products using traditional waterfall methodologies. Businesses, and the data they rely on for decision-making, are moving too quickly. Agile, on the other hand, is enabling teams to build small slices of functionality that deliver business value each and every sprint.

On September 19, 2016, CapTech’s Chief Technology Officer Vinnie Shoenfelder was a guest on Bloomberg Radio’s Taking Stock show with Pimm Fox. In a six-minute segment that originally aired at 3:30 PM EST, Vinnie discussed transforming business for the digital age and how customers are demanding authentic experiences.

On May 9th, Google announced ML Kit for Firebase. Aimed at ordinary developers, rather than data scientists, this release brings machine learning functionality direct to both iOS and Android devices. Six features headline the release: * Facial recognition * Barcode recognition * Image labeling * Landmark recognition * Text recognition * Custom Models Like Apple's CoreML, Google engineered ML Kit to run machine learning models on a device, not to build those models.