Generative AI Engineering for San Francisco businesses

We measure cost per request from the first prototype, then use caching, smaller models for simple steps and tighter prompts, checking each change against the quality tests so savings do not cost accuracy.

Yes. We keep the model behind a thin layer with its own tests, so moving between providers is a measured change rather than a rewrite.

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.