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Chatbots and virtual assistants offer interactive help that makes it easy to monitor, operate, and troubleshoot within your customer channels. Receive alerts, retrieve diagnostic information, configure resources, initiate workflows, and receive AI-based recommendations. It becomes easier for your team to stay updated, collaborate, and respond quickly to incidents, security alerts, and customers.
Deploy a multi-channel, multi-language conversational interface (chatbot) that responds to your customer's questions, answers, and feedback, powered by large language models (LLMs).
Accelerates development and streamlines experimentation by helping you ingest your business-specific data and documents, evaluate and compare the performance of large language models (LLMs), rapidly build extensible applications, and deploy those applications with an enterprise-grade architecture.
Omilia Conversational AI Suirte is an AWS-hosted Customer Service Automation Framework that provides:
Zero-coding, Zero-maintenance
Instantly deployable and configurable Natural Language dialog components.
Now you can easily handle complex tasks, such as
soliciting a credit card number, or a date of birth.
This Guidance demonstrates how to use Amazon SageMaker Unified Studio to create a unified development experience for building, deploying, executing, and monitoring end-to-end workflows across AWS data, analytics, and AI/ML services.
This Guidance demonstrates how to efficiently retrieve data by using the agent-driven framework of Amazon Bedrock to convert natural language queries (NLQ) into SQL queries.
This Guidance shows how you can add generative artificial intelligence (generative AI) to your virtual meetings to translate languages, summarize conversations, and capture live insights.
This Guidance demonstrates how to build on existing enterprise resources to automate tasks associated with the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock.
This Guidance demonstrates how to build an application for search based on the information in an enterprise knowledge base through the deployment of interface nodes, including large language models (LLMs).
