AWS AI services enhanced with FM-powered capabilities

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Amazon AWS

Artificial intelligence (AI) continues to transform how we do business and serve our customers. AWS offers a range of pre-trained AI services that provide ready-to-use intelligence for your applications. In this post, we explore the new AI service capabilities and how they are enhanced using foundation models (FMs).

We focus on the following major updates in this post across key AI services:

  • Amazon Transcribe now offers FM-powered language support across over 100 languages to unlock rich insights.
  • Amazon Transcribe Call Analytics now offers a new generative AI-powered summarization capability (in preview) that automates post-call summarization to improve contact center agent and manager productivity.
  • Amazon Personalize now uses an FM to generate more compelling content and product recommendations
  • Amazon Lex now uses large language models (LLMs) to provide accurate and conversational responses to FAQs (in preview), going beyond task-oriented dialogue

Amazon Transcribe expands language support and supercharges customer service productivity using FMs

In order to build global and inclusive speech-enabled applications that cater to users from diverse linguistic backgrounds, customers seek a truly global AI service that can understand and transcribe a wide array of languages with high accuracy. To help you scale globally, Amazon Transcribe now offers a speech FM-powered automatic speech recognition (ASR) system that expands support to over 100 languages.

FM-powered Amazon Transcribe delivers significant accuracy improvement between 20% and 50% across most languages. Apart from accuracy improvements, the new ASR system delivers several differentiating features across all supported languages (over 100) related to ease of use, customization, user safety, and privacy. Some examples include features such as automatic punctuation, custom vocabulary, automatic language identification, speaker diarization, word-level confidence scores, and custom vocabulary filters. Enabled by the high accuracy of Amazon Transcribe across different accents and noise conditions, its support for a large number of languages, and its breadth of value-added feature sets, thousands of enterprises will be empowered to unlock rich insights from their audio content, as well as increase the accessibility and discoverability of their audio and video content across various domains. All existing and new customers using Amazon Transcribe can experience the performance improvements out of the box, without any API changes.

Carbyne is a software company that develops cloud-based, mission-critical contact center solutions for emergency call responders. Carbyne’s mission is to help emergency responders save lives, and language cannot come in the way of their goals.

“AI-powered Carbyne Live Audio Translation is directly aimed at helping improve emergency response for the 68 million Americans who speak a language other than English at home, in addition to the up to 79 million foreign visitors to the country annually. By leveraging Amazon Transcribe’s new multilingual foundation model powered ASR, Carbyne will be even better equipped to democratize life-saving emergency services, because Every. Person. Counts.”

– Alex Dizengof, Co-Founder and CTO of Carbyne.

In a contact center, agents spend precious time after each call manually summarizing notes, which can impact their productivity and increase call wait times. Managers who have limited time to investigate calls and agent performance spend a significant amount of time listening to call recordings or reading entire transcripts while investigating caller issues. Amazon Transcribe Call Analytics now offers generative call summarization, a generative AI-powered capability that can automatically condense the entire interaction into a concise summary. For example, the following is a sample summary of a 10-minute phone call: “Customer reported that they didn’t receive their order even after 10 days from expected delivery date. The agent offered the customer a free replacement and $10 credit for future purchases. The agent will follow up with the customer in 2 days to confirm the receipt of the replacement order.”

This capability allows agents to spend more time talking to callers waiting in the queue rather than engaging in after-call work, thereby improving customer experience. Managers can review the call summary to quickly understand the context of an interaction without reading the whole transcript.

With AWS post call analytics solution, Principal can currently conduct large-scale historical analytics to understand where customer experiences can be improved, generate actionable insights, and prioritize where to act. We look forward to exploring the post call summarization feature using generative AI in Amazon Transcribe Call Analytics in order to enable our agents to focus their time and resources engaging with customers, rather than manual after contact work

– Miguel Antonio Sanchez, Regional Chief Data Officer, Principal Financial Group.

The following screenshots illustrate how to enable generative call summarization on the Amazon Transcribe console, and an example of a summarized transcript.

Amazon Personalize enables hyper-personalization with FMs

Customers across industries such as retail and media and entertainment are increasingly looking to make content and recommended products more tailored to user interest in order to drive higher engagement. For instance, on streaming platforms, users see the standard “Because you watched” recommendations, and on ecommerce websites, “frequently bought together” is used as a generic tagline. To offer more personalized browsing experiences with titles such as “Rise and Shine” and “Love, laughter, and hijinks,” companies need to allocate resources to generate compelling taglines manually. This is tedious and time consuming.

To help address this challenge, Amazon Personalize now offers the Content Generator—a new FM-powered capability that uses natural language to craft simple and engaging text that describes the thematic connections between recommended items. This enables companies to automatically generate engaging titles or email subject lines, to invite customers to click on videos or purchase items.

In addition, Amazon Personalize now offers Personalize on LangChain to power the journey of customers who want to build their own FM-based applications. With this integration, you can invoke Amazon Personalize, retrieve recommendations for a campaign or recommender, and seamlessly feed it into your FM-powered applications within the LangChain ecosystem.

“We are integrating generative AI with Amazon Personalize in order to deliver hyper-personalized experiences to our users. Amazon Personalize has helped us achieve high levels of automation in content customization. For instance, FOX Sports experienced a 400% increase in viewership content starts post-event when applied. Now, we are augmenting generative AI with Amazon Bedrock to our pipeline in order to help our content editors generate themed collections. We look forward to exploring features such as Amazon Personalize Content Generator and Personalize on Langchain to further personalize those collections for our users.”

– Daryl Bowden, Executive Vice President, Technology, Fox Corporation.

Amazon Lex offers FM-powered capabilities to build bots faster and improve containment

Driven by rising consumer demand for automated self-service, companies are prioritizing investments in conversational AI to optimize customer experience. To that end, AWS recently previewed Conversational FAQ (CFAQ), a new capability from Amazon Lex that answers frequently asked customer questions intelligently and at scale. Powered by FMs from Amazon Bedrock and approved knowledge sources, CFAQ enables companies to provide accurate, automated responses to common customer inquiries in a natural and engaging way. With this innovation, brands can deliver seamless self-service experiences that strengthen customer satisfaction and loyalty.

CFAQ simplifies bot development by eliminating the need to manually create intents, sample utterances, slots, and prompts to handle a wide range of frequently asked questions. It does so with a new intent type called QnAIntent that securely connects to knowledge sources like Amazon Bedrock, Amazon OpenSearch Service, and Amazon Kendra knowledge bases to retrieve the most relevant information to answer a question. Developers maintain control over response content, with the option to summarize retrieved information or use the authorized text as is. This allows highly regulated industries like financial services and healthcare to use CFAQ, enabling you to ensure responses use only compliant language. By streamlining access to relevant knowledge, CFAQ reduces the effort to build bots that handle common customer questions naturally and accurately.

Conclusion

AWS is constantly innovating on behalf of our customers. The latest set of advancements in AI services allow us to deliver more impactful capabilities that help organizations work smarter and provide personalized and intuitive experiences. To learn more about these launches, refer to the following:

About the author

Bratin Saha is the Vice President of Artificial Intelligence and Machine Learning at AWS.

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