Hugging Face

When General Models Aren't Enough

General-purpose frontier models like Gemini are excellent at most voice AI tasks. But some organizations have highly specialized requirements: an insurance carrier whose agents must navigate extremely specific policy language, a healthcare network whose agents handle complex clinical terminology, or a legal firm whose agents need to qualify leads across a nuanced fact pattern. Fine-tuned models trained on your actual call transcripts and domain data can outperform general models on these specialized tasks.

Insurance: Models Trained on Claims Language

A carrier that has thousands of recorded FNOL calls can fine-tune a Llama or Mistral model on that data — training it to recognize the specific phrases, edge cases, and exceptions that appear in their claims calls. Hosted on Hugging Face and connected to Fluents as the conversation engine, that fine-tuned model handles claims intake with higher accuracy than any general model trained on web data.

Healthcare: Clinical Terminology Fine-Tuning

Medical terminology is dense and precise. A clinical AI model fine-tuned on medical literature and patient communication transcripts handles the nuances of healthcare conversations — recognizing symptom descriptions, medication names, and procedure references — with greater accuracy than general LLMs.

Full Data Control With Private Endpoints

Hugging Face Inference Endpoints support private deployment — your model runs on dedicated infrastructure, no data is shared with other users, and the model weights are under your control. For organizations with strict data governance requirements, this is the maximum-control path for LLM deployment in Fluents.