Custom large language model training, fine-tuning and production deployment. We produce domain-specialised models that understand and speak your business with accuracy, context and authority.
Fizon Tech builds and fine-tunes large language models for businesses that need AI which understands their domain — not a generic chatbot. We work end to end: preparing and labelling your proprietary data, fine-tuning open or commercial foundation models (GPT-4/4o, LLaMA 3, Mistral), building Retrieval-Augmented Generation (RAG) pipelines so the model can answer from your live documents, and deploying a low-latency API your team can use in production.
This service suits support teams automating responses, operations teams building internal knowledge assistants, and product teams embedding AI features into their apps. Where data privacy matters, we deploy open-source models on your own cloud or on-premise so nothing sensitive ever leaves your environment. Every model is benchmarked for accuracy, relevance and safety before launch, with guardrails and RLHF alignment to prevent hallucinations — and we retrain it as your data and needs evolve.
From data preparation and fine-tuning to deployment and monitoring, we handle the full AI pipeline end to end.
We structure, clean and label your proprietary data to create high-quality training datasets that give your model accurate domain knowledge.
Starting from foundational models (GPT, LLaMA, Mistral and more), we fine-tune on your data to produce a model specialised for your exact use case.
API-ready deployment on cloud infrastructure or on-premise. Scalable, low-latency serving with full monitoring and cost controls.
Custom AI assistants including customer support bots, internal knowledge bases and document Q&A systems, all trained on your actual business data.
Retrieval-Augmented Generation pipelines that let your LLM answer questions from live documents, databases and knowledge stores in real time.
RLHF and guardrail implementation to ensure your model stays on-topic, accurate and safe, with no hallucinations, no off-brand responses.
We map your exact requirements, covering what the model must know, how it communicates and where it integrates within your systems.
Collecting, cleaning and structuring your proprietary data. We select the right base model and architecture for your domain.
Fine-tuning with iterative evaluation cycles, benchmarking accuracy, relevance and safety against your real-world success criteria.
Production-ready API deployment with dashboards, usage analytics and ongoing retraining as your data and needs evolve.
Fine-tuning adapts a foundation model such as GPT, LLaMA 3 or Mistral by further training it on your proprietary data, so it answers in your domain's terminology, your brand tone and within your rules, instead of giving generic responses.
It depends on the goal. Retrieval-Augmented Generation (RAG) is best when answers must come from live or frequently changing documents. Fine-tuning is best for consistent tone, format and behaviour. We often combine both for accuracy and control.
We work with GPT-4/4o, LLaMA 3, Mistral and other open-source models. We recommend the right base model for your use case based on accuracy needs, budget and data-privacy requirements.
Yes. For sensitive data we deploy open-source models on your own cloud or on-premise infrastructure, so your data never leaves your environment.
We ground responses with RAG, run iterative evaluation cycles against your success criteria, and apply guardrails and RLHF alignment so the model stays accurate, on-topic and safe.
Stop using generic AI and get a model that knows your industry, your tone and your data.
