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Drive Accuracy in Your Voice and Chat Bots

The Solution: LLM-enhanced Intent Recognition for Voice and Chat Bots

Understand your customers’ needs faster and with greater accuracy - The ability to understand your customers’ unique voice and vocabulary are crucial for precise intent recognition and effective first contact resolution.

As businesses navigate the ever-evolving landscape of customer interactions through voice and chat bots, a robust Natural Language Understanding (NLU) system becomes paramount to achieve this understanding.

Traditional NLU systems, although effective, grapple with challenges such as resource demands, and the dependency for time-intensive processes to research, tune, and optimize extensive labeled datasets.

Enter Amazon Bedrock—a cutting-edge large language model (LLM) solution that goes beyond the limitations of conventional NLU systems. Bedrock’s foundational LLMs leverage deep linguistic and contextual understanding, ensuring precise user intent recognition, while maintaining privacy and security. Opportunity lies in seamlessly integrating LLM’s with Amazon Bedrock into your voice and chat bots.

Benefits of LLMs with Amazon Bedrock in Your Voice and Chat Bots:

  • Easy Deployment and Management: Seamlessly integrate with existing AWS Lex and Connect infrastructure, enhancing your existing investments.
  • Improved Accuracy: Experience an improvement in the accuracy of user intent recognition, leading to enhanced customer satisfaction.
  • Reduce Agent Transfers: Correct first-shot classifications decreases intent misclassification and increases first call resolution.
  • Efficient Handling of Unanticipated Intents: LLMs excel in understanding both known and unknown user intents, providing a comprehensive solution for diverse customer interactions.
  • Resource Optimization: Say goodbye to the time-consuming and costly processes to research, tune and optimize utterances and intents.

Beyond this Offering

To continue our collaboration and help guide you through the intricacies of integrating and maximizing the Amazon Bedrock framework within your organization, we suggest the following engagements:

Workshop 1: Framework Deep Dive & Strategic Application: Take part in this free discovery workshop designed to guide you through the intricacies of integrating and maximizing the Amazon Bedrock framework within your organization.

Workshop 2: Strategy and Planning Engagement: Tailored Implementation Roadmap Summary Overview: This workshop offers hands-on guidance and strategic insights for integrating the Bedrock framework, focusing on developing a high-level roadmap and business case to enhance customer experiences and employee productivity.

Bedrock Workshop with Proof of Concept (POC) Summary Overview: This workshop offers an immersive journey into generative AI, tailoring a Large Language Model (LLM) deployment plan to specific data and privacy requirements and guiding participants through AI integration and PoC development.

Bedrock Implementations for Enhanced Customer Experience and Employee Productivity Summary Overview: Leverage AI and data analytics to enhance customer experience and employee productivity. This implementation service focuses on the practical application of strategies from initial discovery workshops and planning engagements for seamless Bedrock integration.

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Pricing Information

This service is priced based on the scope of your request. Please contact seller for pricing details.

Support

How we support you

  • Contact us anytime at aws-support@1904labs.com
  • We can schedule additional meetings as needed
  • We will give you all demonstration materials
  • We will help you present this content to internal stakeholders

What we do

1904labs will help you discover ways to enable your business with Amazon Bedrock, building a roadmap of how you can integrate Amazon AWS products such as Lex, Connect, Lamda, Cohere, and Bedrock. We will partner with you at any level from needs analysis, strategic planning, proof-of-concept development, or custom development and deployment.