What if the context were built in?
Every AI vendor is talking about context. In plain terms, context is the material you give a model along with your question: the documents, the records, the background it needs to answer well. Models can now read more of it at once; they can search the internet for it themselves, and “context engineering” has replaced prompt engineering as the thing consultants sell. The advice for getting better answers from a model is always some version of “give it more context,” and it’s right, as far as it goes.
Why context is king.
Every AI vendor is talking about context. In plain terms, context is the material you give a model along with your question: the documents, the records, the background it needs to answer well. Models can now read more of it at once; they can search the internet for it themselves, and “context engineering” has replaced prompt engineering as the thing consultants sell. The advice for getting better answers from a model is always some version of “give it more context,” and it’s right, as far as it goes.
Case Study: Roberson & Associates
An engineering consultancy accumulates knowledge the same way it accumulates documents: proposals, measurement reports, client analyses, internal policies, years of it. Getting a specific answer means digging through folders and old servers or finding the person who remembers. And in work such as government filings and expert testimony, a missing file or an answer that no one can trace to its source can hurt the firm’s reputation.
The data sovereignty question Palantir didn’t ask.
Palantir’s new paper gives institutions fifteen ways to protect their knowledge from the AI models they use. The better question is why it needs protecting at all.