Category: Artificial Intelligence

Conversational AI solutions, like chatbots and interactive voice response systems (IVR), are a key component of enterprises’ customer service strategy. AWS recently ran a survey, through ESG, on consumers’ opinions of automated customer service solutions like chatbots and IVRs. Conversational AI solutions have come a long way from basic FAQ experiences, and while we see strong positive signals of consumer interest in automated solutions, there are still areas for improvement.

Federated learning (FL) addresses the need of preserving privacy while having access to large datasets for machine learning model training. The NVIDIA FLARE (which stands for Federated Learning Application Runtime Environment) platform provides an open-source Python SDK for collaborative computation and offers privacy-preserving FL workflows at scale. NVIDIA is an AWS Competency Partner that has pioneered accelerated computing to tackle challenges in AI and computer graphics.

The AWS Machine Learning Visionaries Partners Report is a quarterly series that tracks, selects, collates, and distributes horizontal technology capabilities enabled by machine learning in areas that AWS expects to be transformative in 1-3 years. The series’ purpose is to share our insights with AWS Partners and to collect their interest, expertise, and insights in co-building along these prioritized themes. The reports include updates on series topics as we see changes in those areas, and new topics will also be added.

Infusing machine learning into core business processes such as credit scoring creates a competitive edge for banks and financial services institutions. It does not require a data science team, expertise, or platform rollout. Explore an ML-based credit-decisioning model built by ElectrifAi in collaboration with AWS whose model rapidly determines the creditworthiness of a SME, and data-driven, actionable insights reduce the overall processing cost and are consistent and free from any potential human biases.

Many organizations record calls which are potential gold mines of rich insights about customer satisfaction, customer churn, competitive intelligence, service issues, agent performance, and campaign effectiveness. However, the sheer volume of phone calls exceeds a contact center’s ability to review and analyze them in order to glean those valuable insights. Learn how SourceFuse used custom microservices development to design a call center solution for a healthcare customer.

With the rise of voice assistants like Amazon Alexa, customer expectations for handling inquiries and transactions have shifted from the outdated phone keypad, also known as dual tone multi-frequency (DTMF), to modern conversational AI that enables machines to communicate with human beings. In this post, we demonstrate how Presidio implemented conversational AI to check the wait time and reserve a table at a restaurant using Amazon Chime SDK, Amazon Lex, and Amazon Polly.

Featurization is one of the most difficult problems in machine learning. Learn how graph features engineered in Neo4j can be used in a supervised learning model trained with Amazon SageMaker. These novel graph features can improve model performance beyond what’s possible with more traditional approaches. Together, these components offer a graph platform that can be used to understand graph data and operationalize graph use cases.

A digital journey is of strategic importance for many organizations, and digital transformation enabled by cloud technologies has increased efficiency and raised productivity with improved stakeholder experiences. To achieve these outcomes, transformation initiatives need to be holistic, interlinked, and inclusive. Learn how to supercharge customer experiences and make your contact center future-ready by leveraging HCLTech’s Fluid Contact Center Intelligence (Fluid CCI) and AWS AI/ML services.

While utilities have historically been rich with data from customers, programs, and assets, many organizations often manage data in siloes. Source data can also be disorganized, with deficiencies in defined quality assurance and quality control processes. Learn how utilities are successfully embracing Accenture’s data-led transformation (DLT) and leveraging accelerators powered by AWS to reach their business objectives and meet regulatory obligations.

As direct to customer (D2C) gains popularity among retailers, there’s an increasing need to mix online and offline experiences to improve customer engagements and sentiment. One such popular channel is popup stores. This post explores a Capgemini solution that uses Amazon Web Services (AWS) to help retailers engage with customers in a smart way. The solution leverages deep learning to enhance the customer experience through gamification and provides key insights and marketing leads to retailers.