Yes. ProposalAI runs in a dedicated, private cloud where each customer's instance is fully compartmentalized, your data is encrypted in transit and at rest, and enterprise agreements with the underlying AI providers mean your content is never used to train their models. For contractors handling CUI or ITAR data, ProposalAI operates in a secure environment aligned to federal cybersecurity requirements and is built around the FAR—giving you the isolation and compliance posture generic, public LLMs can't offer.

No—ProposalAI is closed to your own library and is engineered to be conservative, so it draws only from the content you provide rather than inventing answers. When it doesn't have relevant past performance to answer a requirement, it tells you "no relevant content found" instead of fabricating something, and it runs a filtering layer to catch errors before results are returned. A human stays in the loop at every step, with full visibility into which source documents each answer came from.

ProposalAI works best when you load your library—past proposals, capabilities content, resumes, and other corporate documents—into the platform, and the more you provide, the more the output sounds like you. Setup is simple: you bulk-upload your documents (one customer loaded roughly 20,000 in a couple of hours), and the platform automatically parses, tags, and indexes everything so it's ready to reuse. The more you use it, the more it learns your tone, terminology, and the way you write.

Yes. ProposalAI gives you decision checkpoints throughout the workflow, so you can gauge fit before committing real effort. At the solutioning step it searches your library against the key requirements and flags where you have little or no relevant past performance—an early signal to team up or pass—and the Champ assistant lets you ask, in plain language, questions like "have we done this kind of work before?" to assess win probability and surface relevant experience.

ProposalAI is purpose-built for government proposals, pairing a best-of-breed set of LLMs with a structured proposal workflow—rather than leaving you to manage scattered chat threads in a general tool. Through Champ it delegates each task to the best-fit model instead of locking you into one, it grounds every answer in your secure library with source citations, and it layers shredding, solutioning, compliant outlining, and requirement mapping on top—reducing the manual effort, version-control problems, and security and accuracy risks buyers consistently cite with generic chatbots.