Step 3 of 4 · Deploy
RAG & LLM Fine-Tuning
Retrieval pipelines and fine-tuned open-weight models, built on your domain data and deployed in your cloud tenant with a zero-egress architecture for sensitive data. Every fine-tuned model's weights belong to you. Model-agnostic delivery, no platform lock-in.
RAG Pipelines
Real-time answers, grounded in your documents.
Retrieval-augmented generation over your product docs, regulatory rulebooks, or knowledge base, with citation trails so every answer is traceable to a source. This is the technique behind our regulatory Q&A and branch-copilot use cases.
LLM Fine-Tuning
A model that speaks your domain, not a generic assistant.
QLoRA (preferred, compute-efficient) or full fine-tuning based on your budget and task. Built on open-weight base models (Llama, Mistral, Phi) selected for commercial-use licensing, not vendor preference.
Delivery Framework
Six phases, from data governance to handoff.
Data Assessment & Governance Setup
Inventory available domain data. Assess quality, volume, and licensing. PHI handling protocol established. Baseline model evaluation.
Base Model Selection & License Review
Select an open-source base model based on task, context window, and confirmed commercial-use license.
Fine-Tuning Run
QLoRA (preferred) or full fine-tune based on compute budget. Hyperparameter configuration. GPU selection and cost estimate agreed upfront.
Evaluation Framework
Domain-specific benchmarks. A human evaluation sample, with clinician sign-off required for any clinical use case. Hallucination rate, latency, and task accuracy measured.
Deployment
Model served in your cloud tenant. Zero-egress architecture for PHI and other sensitive data. Inference cost monitoring configured.
Handoff & Knowledge Transfer
Your team trained on prompt engineering, fine-tuning iteration, and model evaluation. IP assignment: all fine-tuned weights belong to you.
IP assignment: standard in every SOW
Upon full payment, we assign to you all rights, title, and interest in the work product created specifically for your use case, including fine-tuned model weights. We retain only our pre-existing methodologies and general know-how, never your data, your model, or your IP.
FAQ
Common questions about RAG & LLM Fine-Tuning.
What's the difference between RAG and fine-tuning?
RAG grounds answers in your documents with citation trails; fine-tuning (QLoRA or full) adapts an open-weight model to speak your domain.
Which base models does Nyveon.AI use?
Open-weight models (Llama, Mistral, Phi) selected for commercial-use licensing, not vendor preference.
How long does a RAG or fine-tuning engagement take?
Typically 6–12 weeks across six phases, from data governance to handoff.
Who owns the fine-tuned model weights?
You. IP assignment is standard in every SOW, and Nyveon.AI retains only its own pre-existing methodologies.