Fine-tuning adapts an existing foundation model to your unique dataset. Instead of attempting to retrain billions of parameters, modern techniques like LoRA (Low-Rank Adaptation) allow us to update only the portions of the network responsible for domain-specific behavior. This keeps training efficient, cost-effective, and achievable even for small startups.

Custom model training involves several components: dataset cleaning, curation, augmentation, quality filtering, hyperparameter tuning, evaluation loops, and security considerations. It also includes cross-domain adaptability—text for chat or reasoning, images for classification or extraction, or signals for time-series and sensor intelligence. The result is a model that doesn’t just perform tasks, but performs your tasks the way you need them done.