ML Models Built for
Your Data. Your Domain.

Generic AI APIs are trained on broad, public data and perform well for general tasks. But for domain-specific problems detecting your specific fraud patterns, recommending your specific product catalogue, classifying your specific support tickets a model trained on your data will significantly outperform any generic solution. The accuracy gap is typically 10-40% in our experience, and that gap directly translates to business impact.

It depends entirely on the problem. Image classification models can work with a few thousand labelled examples. Text classification can work with as few as 500-1000 examples with modern techniques like few shot learning and transfer learning. Fraud detection typically needs millions of transactions with labelled fraud cases. We always audit your data first and tell you honestly whether custom training is viable and what would improve it if not.

You own everything: the model weights, the training code, the data pipelines, and all associated IP. We hand over full ownership at project completion and document everything so your team can maintain, retrain, and extend the model independently.

Fine tuning adapts a model's weights to your specific data, vocabulary, writing style, and task so it becomes genuinely specialised for your domain rather than a general model with a long prompt. Fine tuned models are typically faster, cheaper per inference, more consistent in format, and significantly more accurate on domain-specific tasks than their prompted equivalents. They also run independently not dependent on a third party API.

Yes. We deploy models as containerised API services that can run on your AWS, GCP, or Azure environment or on-premise if required. For regulated industries, fully private deployment is often mandatory and we design for this from the start.

We implement bias evaluation as part of the standard model development process testing performance across demographic segments, protected attributes, and edge cases before deployment. Where fairness is a regulatory requirement (e.g., credit scoring, hiring), we apply fairness aware training and provide documentation for compliance review.