We fine-tune leading large language models and vision-language models on your proprietary data — so your AI understands your domain, your tone, and your compliance requirements.
Generic models hallucinate on niche terminology. A fine-tuned model trained on your clinical notes, legal briefs, or product catalogue delivers reliable, context-aware outputs every time.
Deploy on your preferred cloud infrastructure or on-premises. We support models that meet ASD Essential Eight, APRA CPS 234, and Privacy Act requirements out of the box.
A smaller, well-tuned model outperforms a large general model at a fraction of the cost. Enterprises running millions of requests monthly see 60–80% reduction in inference spend.
Fine-tuned VLMs for radiology report generation, clinical note summarisation, and diagnostic support — built to meet TGA and My Health Record compliance requirements.
LLMs trained on Australian case law, Corporations Act, contract templates, and AUSTRAC guidance for accurate legal research, drafting, and AML compliance.
Models fine-tuned on ASX filings, ASIC guidance, and internal risk data for faster, more accurate financial analysis.
Product description generation, visual search, and personalised recommendation engines tuned on your catalogue — proven across major Australian retail and marketplace brands.
LLM fine-tuning is the process of further training a pre-trained large language model on your domain-specific dataset so it produces more accurate, relevant, and on-brand outputs. Instead of starting from scratch, you leverage a model's existing language capability and specialise it for your exact use case.
LLM fine-tuning focuses on text-only tasks such as summarisation, Q&A, and code generation. VLM (Vision-Language Model) fine-tuning involves both images and text, enabling use cases like medical image analysis, document understanding, and visual search.
It depends on the technique. LoRA and QLoRA fine-tuning can achieve strong results with as few as 500–2,000 high-quality examples. Full fine-tuning typically requires tens of thousands. We help you assess and, if needed, augment your existing dataset.
Yes. HypeGenAI works with enterprises across regulated industries. We offer on-premises or region-specific cloud deployments to satisfy Privacy Act, APRA, and ASD requirements, and we sign data processing agreements as standard.
Most engagements run 4–8 weeks from data audit to production deployment. Simple LoRA fine-tunes on clean datasets can be completed in 2–3 weeks. Complex multi-modal or multi-task projects may take longer. We provide a precise timeline after the initial scoping call.
Get a scoping call and find out which fine-tuning approach is right for your use case and budget.