TRUSTED DATA FOR AI
How to trust the data your AI agent uses.
THE PROBLEM
A model answers from whatever you feed it. If the data carries no source, you cannot tell a real figure from a planted one, or a first-hand record from a copy of a copy. The output looks the same either way. That is the risk a builder carries: the data decides the answer, and most data arrives with nothing to vouch for it.
THE ANSWER: A LABEL YOU CAN READ
Trust comes from seeing where data came from. DRM3 keeps a fact record where every fact and every dataset carries a receipt, the way a package carries an ingredient label: the source, the time it was read, and each step taken since.
A signature on that receipt proves who recorded it and that it has not changed since. It does not certify that the claim is correct; you can sign a mistake. What it gives you is a source to check and a chain to follow, so you can judge the data instead of taking it on faith.
WHAT YOU GET
Ask, get a sourced answer
Ask the record a question and get a written answer with its sources cited inline and a receipt on the answer.
Ask the recordPull it through an API
Read signed slices of the record from your own code: keyed lookups and streams, each with a receipt you can check offline.
The data doorsFind trusted datasets
Browse signed, sourced data by subject. Every dataset shows its record: the source, the time it was read, and each step since.
The recordA data cooperative
The record is pooled and shared. Data comes in signed, and what you pull carries the same label it arrived with.
How provenance worksCHECK IT YOURSELF
Every receipt is verifiable. Re-hash a record in your browser, check any signature against its signing key, and follow the chain back to the source. The keys are public and listed on the record.
Put a label on the data under your agent.