Think of training as teaching someone a language from birth, while fine-tuning is like giving an expert additional specialized knowledge. Training an LLM from scratch means feeding it massive amounts of general text data to learn language patterns - it's expensive and time-consuming. Fine-tuning takes an already-trained model and teaches it your specific terminology, processes, and style using your business data. Fine-tuning is much faster, cheaper, and usually gives you exactly what you need without starting from zero.

You absolutely can deploy LLMs on-premises if data security and control are priorities for your organization. While cloud deployment is often easier and more cost-effective, we can set up on-premises solutions using your existing infrastructure or help you build the necessary computational environment. The choice depends on your security requirements, budget, and technical capabilities - many of our enterprise clients prefer on-premises for sensitive data while using cloud solutions for less critical applications.

Yes, we're experienced with virtually every data format out there - from structured databases and spreadsheets to unstructured documents, emails, PDFs, and even legacy system outputs. Our team handles the data preprocessing and conversion to make it LLM-ready, whether it's cleaning messy data, standardizing formats, or extracting valuable information from complex sources. We work with what you have rather than forcing you to change your existing data infrastructure.

Adding new data or retraining is actually straightforward and something we plan for from the beginning. We build your LLM pipeline to accommodate updates - whether it's monthly data refreshes, quarterly retraining, or adding entirely new data sources as your business evolves. The process is much faster than the initial training since we're building on an existing foundation, and we can often do incremental updates without disrupting your live system.