What We Do
Generic AI models are trained on the internet. Your business is not the internet. When the language model does not understand your industry terminology, your document formats, or your classification taxonomy, generic outputs are the consequence. Custom model training fine-tunes a base model on your proprietary data so the AI understands your world specifically.
It knows the difference between the contract terms your legal team uses, the product categories in your catalog, and the customer intents your support team encounters. We train language models, classification models, and prediction models on your data and deliver production-ready models you own outright. No vendor lock-in. No subscription required to run a model trained on your own information.
How We Work
Training begins with data preparation: collecting labeled examples, cleaning inconsistencies, balancing class distributions, and structuring the dataset for the training task. We select an appropriate base model from the open-source or commercial ecosystem and run fine-tuning experiments with different hyperparameter configurations. Each experiment is evaluated against a held-out validation set using task-specific metrics. The best-performing configuration is then trained on the full dataset and evaluated against your defined acceptance benchmarks.
Before deployment we document the training process, evaluation results, known edge cases, and operational limits. You receive the model weights, the training dataset, and complete documentation. The model can be served in your infrastructure or deployed through a cloud provider of your choice.