Category: Artificial Intelligence

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voice assistants like Amazon Alexa, customer expectations for handling inquiries and transactions have shifted from the outdated phone keypad, also known as dual tone multi-frequency (DTMF), to modern conversational AI that enables machines to communicate with human beings.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.

Like many of today’s leading media and streaming platforms, PBS wanted to take its overall user experience to the next level. That’s why PBS approached AWS Premier Tier Consulting Partner ClearScale, a leader in machine learning. ClearScale came up with a detailed roadmap for tackling PBS’s recommendation system project that included data operations, MLOps, and demonstrational user interface. Together, PBS and ClearScale decided to move forward with an AWS-powered solution on top of Amazon Personalize.

Data mesh is emerging as a paradigm for generating data-driven value and is gaining real-world adoption within industries like financial services and automotive. Learn about the user journeys of two types of data consumers in a mesh platform: business intelligence and data scientists. Explore how BI and AI/ML overlap within a set of data domains, and how a platform architecture further enables the desired experiences within a data mesh.

Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to Amazon EKS.

Industry analyst firm Gartner has published its annual report evaluating cloud AI developer services, the 2022 Magic Quadrant for Cloud AI Developer Services (CAIDS). AWS was once again named a Leader and placed highest among 13 recognized vendors for “Ability to Execute.” Choosing the right provider for cloud AI developer services is critically important right now. AWS Partners can leverage this report with their customers to showcase the value that AWS will bring to them.