A persona built on someone else's RAG platform is a prompt sitting on their infrastructure — it doesn't give you an asset. UltimateModel fine-tunes a small model on your frameworks, sessions, and material, and the weights export with you.
Trained, not prompted
Upload your course content, call transcripts, frameworks, or written material. UltimateModel fine-tunes the model itself with LoRA, so your approach lives in the weights, not in a system prompt sitting in front of every request.
You keep the asset
Export your fine-tuned model as GGUF or publish it to Hugging Face, and run it wherever you choose. A persona hosted on someone else's platform stays on their platform — a model you've trained is portable.
Gets sharper with use
Real interactions feed preference optimization that keeps the model improving — the self-improving flywheel ships on Professional and above, with GRPO online reinforcement learning and ORPO preference alignment on Business. Every new version is measured head-to-head against the one you're currently serving — if it doesn't clear the bar, your current model keeps serving and the new version is held instead of shipped.
How it works
Documents, transcripts, PDFs, text, audio — whatever defines your knowledge, your voice, or your operations.
LoRA fine-tuning turns your upload into a custom model. No ML background or infrastructure required.
Serve your model through a hosted chat page, an embeddable widget, or an OpenAI-compatible API endpoint.
Real conversations feed preference optimization — self-improving flywheel on Professional and above, GRPO/ORPO on Business. New versions are measured against the one you're currently serving before anything changes what your users see.
Your expertise took years to build. Put it into a model that's actually yours — trained on your material, exportable on every plan, and improving from every real conversation.
Keep your weights. Cancel anytime.