From the University of Chicago and MIT
invocate builds on over a decade of research at the University of Chicago and MIT in data systems, data markets, privacy, AI, and data ecosystems: how organizations find data worth combining, what makes sharing it safe, and what a market for data would have to look like to function.
Selected papers
Peer-reviewed work underpinning the escrow model.
Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia
Establishes how parties can be incentivized to pool data, and what a functioning market for it requires the economic groundwork under transactable data access.
Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow
The data escrow itself: delegated computation that parties can trust without surrendering their data, producing an auditable record of what ran. This is the architecture the Escrow Agent is built on.
Data Ecology: Understanding and Designing Data Ecosystems
Treats the flow of data through an economy as something that can be deliberately designed rather than left to chance, including the flows that never happen because no party trusts the exchange. It names the data escrow as the intervention that unblocks them.
How Large Language Models Will Disrupt Data Management
Our vision, written early, for what language models change about the way software understands and uses data. It is why invocate treats AI agents, not only people, as parties to a data transaction.
Data Market Platforms: Trading Data Assets to Solve Data Problems
The earliest statement of the thesis: data stays locked away because owners lack both the information about who needs it and the incentive to make it usable, and a market is what supplies both.