IPVault vs. pasting into ChatGPT
Pasting into ChatGPT hands your whole document to one company and asks you to trust its promises and its defaults. IPVault never lets any single AI see the whole thing. Here is the difference, point by point, so you can decide what your work is worth.
Who sees your whole document
When you paste into ChatGPT, one company receives your entire document. With IPVault, your work is split into small masked pieces spread across several AI models, and no single model ever receives more than a small share. Nobody gets the whole picture.
Training on your work
Consumer AI tools may use your inputs to train their models, and the defaults change. IPVault never uses your work to train any AI model, and because no single model sees the whole thing, there is nothing complete for any model to learn from.
Control over the most sensitive parts
In a chatbot, everything you paste is sent, all or nothing. In IPVault you can mark the passage that carries the real value, the invention claim or the key scene, as extra sensitive, and it is kept on your device and never sent to any AI, while the rest still gets help.
Proof you can keep
A chatbot gives you no record of what happened to your data. IPVault gives you a tamper-evident, time-stamped log of which AI handled which piece and when, plus a document fingerprint you can keep as proof of existence. That is the difference for a patent, a trade secret, or a client file.
Questions, answered
Is IPVault a replacement for ChatGPT?
It is a safer way to use AI on confidential work. It still uses leading AI models to do the work, but it splits your document so no single model sees the whole, masks names and secrets, and never trains on your input.
Why not just use ChatGPT Enterprise?
Enterprise tiers still ask you to trust one vendor with the entire document. IPVault removes the need to trust any single vendor, because no single one ever receives more than a small, masked share of your work.
Does IPVault give worse results because it splits the work?
You still get help from leading models, and a second, different model double-checks a sample so you can see where they agree. The most sensitive parts you choose to hold back are kept as written, not degraded.