Generative AI Engineering for Boston businesses

Drafts are built only from the sources you provide, every statement links to its source, and reviewers approve the text. We measure accuracy on a test set before anyone relies on it.

Yes. We can run models in your own cloud account so unpublished data never leaves your environment.

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.