Benefits
| Benefit | Description |
|---|---|
| Better Accuracy | Smaller foundation models (under 10B parameters), fine-tuned for conversational AI, outperform prompting larger generative models directly. |
| Faster Responses | Smaller models co-hosted with the platform deliver low-latency responses suited for digital and voice production use cases. |
| Ready to Use | Pre-fine-tuned models deploy immediately—no in-house AI expertise or tuning cycles required. |
| Data Security | Fully integrated into the platform, enforcing enterprise-grade data confidentiality, privacy, and governance. |
Model Fine-Tuning Process
- Collect Data — Gather a task-relevant dataset to serve as training material.
- Select a Base LLM — Choose a pre-trained model suited to the task.
- Train — Adjust model parameters using the task-specific dataset to learn conversation patterns.
- Test and Refine — Evaluate on a validation dataset and iterate to achieve optimal results.
Supported Features
| Feature | Description | Learn More |
|---|---|---|
| Answer Generation | Generates answers from data ingested into Search AI using RAG. | Search AI GenAI Features |
| Conversation Summary | Generates concise summaries of agent-user-human interactions; integrates with Contact Center and third-party apps via API. | Automation AI GenAI Features |
| DialogGPT - Conversation Orchestration | Manages conversation flow, identifies intent, and routes conversations to the correct AI Agent in universal apps. | DialogGPT Conversation Orchestration |
| Rephrase Dialog Responses | Rephrases AI Agent responses based on conversation context and user emotion for more empathetic interactions. | Automation AI GenAI Features |
| Rephrase User Query | Expands and rephrases user queries using app domain knowledge and conversation history to improve NLP accuracy. | Automation AI GenAI Features |
| Vector Generation (Image & Text) | Creates vector embeddings for text and image data in Search AI; converts queries to embeddings for vector search. | Search AI GenAI Features |
XO GPT Model Specifications
XO GPT models are fine-tuned for specific conversational AI tasks. The pages below cover each model’s design, benchmarks, fine-tuning parameters, and version history, along with shared information on the model building process and live deployment versions.| Document | Description |
|---|---|
| Model Specifications | Live model versions, the model building process, benchmarks index, and roadmap. |
| Answer Generation Model | RAG-based model that generates accurate answers from domain-specific ingested data. |
| Conversation Summarization Model | Abstractive summarization model for agent-customer interaction transcripts. |
| DialogGPT Model | Intent prediction model for multi-turn conversation orchestration. |
| Response Rephrasing Model | Rephrases AI Agent responses to be more empathetic and contextually appropriate. |
| User Query Paraphrasing Model | Expands and rephrases user queries to improve downstream NLP accuracy. |