Updates and perspectives from our team

Customer service chatbots are some of the most widespread uses of AI amongst companies. Find out how intent recognition can deliver benefits to your sales pipeline and customer support efforts. ML can now understand users’ goals, paving the way for automated, personalized responses.

Product recommendations are essential to increasing revenue and user engagement and AI can significantly scale them . This blog post details our work with two major ecommerce sites to help them make the most of their existing data in a recommender system.

From social media to news coverage, there's a wealth of data for sentiment analysis to help better understand the perception of your brand or business.

OCR is one of the most widely used ML techniques. We explore three use cases showing how OCR has improved accessibility on a global scale.

A look at what companies need to do, not just to survive, but to flourish within the new landscape forged by the advent of ML.

Named entity recognition (NER) is a key component of natural language processing (NLP) .In this post, we’re going to look at how companies and organizations have successfully deployed the technique to help them achieve their goals.

We explore the final two areas where the concealed complexities of machine learning can create costly surprises: configuration and R&D debt.

Messy real-world input data can quickly wreck havoc on a machine learning (ML) system that isn’t adequately prepared. Here, I show you how to stay ahead.

In this article we talk about how the algorithms ML models learn are determined by input data, rather than by a programmer’s handwritten rules—has profound system-level performance effects in ML and introduce the topics of covariance and prediction shift detection.

What is sentiment analysis and how does it work? We show you can apply this ML technique in your business.