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The Kore.ai XO GPT module provides fine-tuned LLMs for enterprise conversational AI. These models are optimized for accuracy, safety, and production efficiency. Current capabilities: Answer Generation, Conversation Summarization, User Query Rephrasing, AI Agent Response Rephrasing, Vector/Embedding Generation (Text and Image), and Intent Resolution (DialogGPT).

Benefits

Model Fine-Tuning Process

  1. Collect Data — Gather a task-relevant dataset to serve as training material.
  2. Select a Base LLM — Choose a pre-trained model suited to the task.
  3. Train — Adjust model parameters using the task-specific dataset to learn conversation patterns.
  4. Test and Refine — Evaluate on a validation dataset and iterate to achieve optimal results.

Live Model Versions and Supported Languages

The table below lists all currently deployed XO GPT models.
For non-English languages, XO GPT supports industry-established generic use cases. For additional language-specific support, use the Agent Platform.

Supported 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.

XO GPT Feedback Submission

Kore.ai incorporates customer feedback into ongoing model improvements. Effective feedback helps prioritize issues, identify recurring patterns, and drive targeted retraining cycles. To submit feedback, open a support ticket with your sample set, error category, use case, and expected vs. actual outputs. See XO GPT Feedback Submission for the full guide, issue categories, and the feedback workflow.