Generative AI Engineering for Seattle businesses

Yes. We work with your chosen provider and keep the model behind a layer with its own tests, so you can change providers later.

We agree a test set of real questions with your team, measure accuracy and safety against it, and rerun it on every change.

Retrieval-augmented generation (RAG) finds the most relevant passages in your own content and gives them to the language model with the question, so answers are grounded in your data and can cite their sources.

Often not. Good retrieval and prompt design solve most enterprise use cases. We recommend fine-tuning only when evaluations show it clearly improves quality, cost or speed.

We design around your security requirements: private deployments where needed, access controls applied to retrieval, no training on your data without consent, and full logging.