AI chatbot
Easily build custom and pre-built AI-powered chatbots
Develop chatbots, AI agents, and human-like contact center experiences with Google’s industry leading AI.
New customers get up to $300 in free credits to start building a chatbot.
Overview
What is an AI chatbot?
Artificial intelligence (AI) chatbots are apps or interfaces that can carry on human-like conversation using natural language understanding (NLU) or natural language processing (NLP) and machine learning (ML). AI chatbots differ from standard chatbots in that they leverage large language models (LLMs) versus traditional conversation flows and pre-programmed responses to generate responses to text and voice inputs.
What are the benefits of an AI chatbot?
AI chatbots can improve customer experiences with virtual agents trained on a business's content and data, lower costs, and scale customer support. AI chatbots can act as the sole point of customer contact, support human agents at call centers, recommend answers generated on the fly, and field frequent customer inquiries.
AI chatbots vs chatbots vs virtual agents
Non-AI chatbots use scripted dialog and cannot generate any responses that were not pre-programmed into the chatbot.
AI chatbots leverage AI, ML, NLU, NLP, and LLMs to deliver human-like responses to human input. AI chatbots are trained on large amounts of data and use ML to intelligently generate a wide range of non-scripted, conversational responses to human text and voice input.
Virtual agents are AI bots that can be specifically trained to interact with customers in call centers or contact centers.
What are common AI chatbot uses?
AI chatbots are commonly used as contact center solutions, real-time assistance to human agents, generative chatbots, voice capabilities, and sentiment analysis. Google Cloud's Conversational Agents can help you create virtual agents that use generative AI to seamlessly switch between topics and operate across multiple channels 24/7. Vertex AI Agents enables developers to build AI-powered chat apps. And Customer Engagement Suite with Google AI improves call center and customer service experiences.
RAG and chatbots
Some chatbots utilize Retrieval Augmented Generation (RAG) to overcome the limitations of their pre-programmed responses and access a wider pool of information. When a user asks a question, a RAG-powered chatbot doesn’t have to just rely on pre-written scripts. Instead, it may leverage RAG to search through external knowledge bases and documents relevant to the user's query. This could include internal wikis, product documentation, or even publicly available information on the internet.
How It Works
AI chatbots use LLMs (large language models) to make large amounts of data—including websites, documents, and inventories accessible through conversational inputs and outputs. In short, this means that AI chatbots can have human-like conversations about whatever data the LLM is trained on, enabling AI to offer support, insights, and commentary in chat format.
AI chatbots use LLMs (large language models) to make large amounts of data—including websites, documents, and inventories accessible through conversational inputs and outputs. In short, this means that AI chatbots can have human-like conversations about whatever data the LLM is trained on, enabling AI to offer support, insights, and commentary in chat format.
Common Uses
AI agents
Build your own AI agent
In this hands-on codelab series, you'll embark on an exciting journey to create your very own intelligent AI agent using Google's Agent Development Kit (ADK).
We'll start with the absolute essentials, guiding you through setting up your development environment and crafting a foundational conversational agent. By the end of this codelab, you'll have built your first interactive AI, ready to be expanded upon in subsequent parts of this series as we transform it into a sophisticated Multi-Agent System (MAS).
How-tos
Build your own AI agent
In this hands-on codelab series, you'll embark on an exciting journey to create your very own intelligent AI agent using Google's Agent Development Kit (ADK).
We'll start with the absolute essentials, guiding you through setting up your development environment and crafting a foundational conversational agent. By the end of this codelab, you'll have built your first interactive AI, ready to be expanded upon in subsequent parts of this series as we transform it into a sophisticated Multi-Agent System (MAS).
Virtual agents for contact centers
Build an AI agent for contact centers
Build your own AI-powered contact center with Contact Center as a Service (CCaaS), an AI-driven contact center platform that is built natively on Google Cloud. CCaaS works alongside CRMs and features Conversational Agents for building a chatbot, Agent Assist for real-time assistance to human agents, and Conversational Insights for identifying call drivers and sentiment.
How-tos
Build an AI agent for contact centers
Build your own AI-powered contact center with Contact Center as a Service (CCaaS), an AI-driven contact center platform that is built natively on Google Cloud. CCaaS works alongside CRMs and features Conversational Agents for building a chatbot, Agent Assist for real-time assistance to human agents, and Conversational Insights for identifying call drivers and sentiment.
Conversational AI
Build a conversational AI agent
Build and train Conversational Agents to answer questions, make recommendations, and handle concurrent conversations with your end users. Conversational Agents natural language module understands the nuances of human language, translating end-user text or audio during a conversation to structured data that your apps and services can understand.
How-tos
Build a conversational AI agent
Build and train Conversational Agents to answer questions, make recommendations, and handle concurrent conversations with your end users. Conversational Agents natural language module understands the nuances of human language, translating end-user text or audio during a conversation to structured data that your apps and services can understand.
Generative AI knowledge base solution
Deploy a gen AI knowledge base solution to enable customer self-service
Launch a Google-recommended application that extracts question-and-answer pairs from your documents. Based on the output from the application, you can train and fine-tune a prompt-based AI model, which can be used as an example for other customer self-service use cases.
How-tos
Deploy a gen AI knowledge base solution to enable customer self-service
Launch a Google-recommended application that extracts question-and-answer pairs from your documents. Based on the output from the application, you can train and fine-tune a prompt-based AI model, which can be used as an example for other customer self-service use cases.
