DialogGPT addresses the complexities of managing natural, context-rich conversations at scale within enterprise environments.
Traditional Conversational AI systems often struggle with maintaining conversational fluidity while adhering to strict business rules, leading to interactions that can feel rigid or disjointed.
DialogGPT bridges this gap by autonomously orchestrating dialogues across multiple topics through Dialog Tasks, effectively balancing structured business logic with the seamless conversational experience users expect.
By leveraging advanced text embeddings and generative models, it contextually understands user inputs and identifies optimal paths for request fulfilment without the need for extensive training data.
Additionally, DialogGPT solves the problem of disparate enterprise environments, integrating intent-driven dialog flows, search, generative AI conversational experiences, Retrieval-Augmented Generation (RAG), and more, into a cohesive and efficient system.
This innovation streamlines the deployment of virtual assistants, enhancing user satisfaction and operational efficiency.
DialogGPT is an agentic orchestration engine which powers natural conversations at scale, with autonomous orchestration across multiple topics through Dialog Tasks.
DialogGPT has at its aim to balance defined business rules and creating natural and resilient conversations.
The framework makes use of a combination of text embeddings and generative models, it contextually understands user input and identifies optimal paths for request fulfilment.
DialogGPT processes user input and conversation history to shortlist relevant chunks, which are segments from dialogs, FAQs or Search AI embeddings stored in a vector database.
It rephrases the input to enhance retrieval accuracy and uses a Retrieval-Augmented Generation (RAG) pipeline to select precise chunks.
This process operates independently of the Search AI pipeline, ensuring a streamlined selection of relevant content.
The Agentic Conversation Orchestrator analyses the retrieved chunks, user input, and context to identify the most appropriate intent.
Leveraging a language model, DialogGPT resolves ambiguities by ranking options or prompting the user for clarification, and it determines the execution order for multi-intent scenarios.
System intents, such as repeating, pausing, or ending conversations, are managed through predefined handlers.
The orchestrator classifies fulfillment strategies into single, multiple, ambiguous, or system intents and selects the optimal approach for execution.
DialogGPT triggers the resolved intent with the appropriate fulfilment action.
It executes dialog tasks or FAQs, generates system intent responses, and asks for clarification in ambiguous cases.
For multi-intent scenarios, tasks are executed sequentially or in parallel based on dependencies.
Upon task completion, the system delivers a contextually relevant response or fulfills the user's request, adhering to enterprise business rules and interaction modes, such as text or voice.
Debugging and traceability for a system like DialogGPT is of utmost importance to understand how the Agentic Engine is making decisions, in order to tweak the engine.
XO offers detailed insights into the DialogGPT behaviour and execution flow. Allowing you to observe, discover and trace each step of the DialogGPT process and its outcomes.
Traces include complete request and response details for every LLM call, clearly showing what happens at each stage.
This information lends insight and understanding and to DialogGPT’s decision-making and execution for better debugging and optimisation.
When DialogGPT processes a user utterance, the traces capture key details such as:
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