Many organizations – and namely developers – have tried putting a chatbot together by using Microsoft Bot Framework, Google Dialogflow, or Amazon Lex. These take months to build because they rely on predefined conversation flows (aka decision tree logic built out by developers). If a user asks the bot a question outside of this conversation flow, they get a “I don’t know” response. We’ve all been there, and let’s be honest, we’re frustrated with the chatbot experience today because traditional chatbots can’t answer our questions. We then are put in a queue to connect with a live service agent.
But imagine if a chatbot actually works. A true digital assistant (also called a copilot) that can leverage agents. Agents connect to third-party systems to access real-time data. And this digital assistant can not only answer your questions, but also help you complete tasks.
Overcoming the limitations of traditional bots
One of the greatest challenges faced by traditional bots is that they can’t handle when a user changes the chat conversation. Users switch topics or change their mind mid-chat, similar to how they’d write something in a chat message to a colleague, family or friend. But traditional bots can’t keep up because they’re built on rigid flows, which by the way, designing these conversation flows require a LOT of coding, and even then, it’s impossible to account for every use case.
This is where a reasoning engine comes into play. Unlike traditional bots, which can become confused when users change topics, our copilots keep track of the entire conversation history. The copilot digital assistant can respond to shifts in conversation without losing context or requiring a restart.
Retrieval Augmented Generation (RAG) and Image AI
What is RAG? RAG, or Retrieval Augmented Generation, combines a large language model (LLM) with a vector database which stores an organization’s content. That LLM will call upon multiple sources of content to answer the user’s question. Let’s say a hotel guest asks a digital assistant on the hotel’s website “What is your spa like?” The copilot will provide an answer and can even use Image AI where it matches the answer with a relevant image to create a response.
At the core of ai12z’s copilot platform is its advanced reasoning engine, which is powered by an LLM. It can manage complex, multi-step processes using the available agents and a system prompt that guides its behavior. The system prompt defines the AI’s role, tone, and boundaries, ensuring that the AI stays on track and responds appropriately to every interaction.
Agents can access real-time data
What makes a copilot digital assistant truly revolutionary is when it can integrate with external systems through the use of agents.
What’s an agent? An agent will connect with third-party systems so it can access real-time data from those systems and provide that back to the digital assistant. This enables the digital assistant to provide a personalized experience to the user. Whether syncing with a CRM, Google Maps, or backend systems via REST APIs, these agents can then enable the digital assistant to automate workflows and complete tasks.
ai12z’s copilot platform has out-of-the-box agents, which come with detailed descriptions and parameters, enabling the AI to know exactly when and how to call upon them to solve real-world problems. And, an agency or organization can easily create their own custom agent.
Imagine a hotel guest asking for a late checkout. Instead of following a rigid flow, the copilot creates a plan. First, it identifies the guest’s details using a form agent that requests their name and room number. It then calls another agent connected to the hotel’s booking system to check if the room is available for a late checkout. Once confirmed, it informs the guest and even sends them an email with the details – all in real time.
Your copilot digital assistant does not have to be limited to handling unstructured content. Its agents can integrate with structured data systems, such as Product Information Management (PIM) systems, or use GraphQL agents to filter and refine responses from existing data sources.
Every brand will have an AI assistant. By combining advanced reasoning with your organization’s content and integration with third-party systems to access real-time data, copilot digital assistants can deliver a personalized and context-aware experience that not only answers users’ questions but also can help users complete tasks. Having a generative AI platform equipped with advanced reasoning engines will transform how businesses handle real-time conversations and complex workflows.