Key Notes
- Space and Time Enterprise launches with Maple Finance, Indomobil, Trenez and The Data Foundation as partners, targeting private data workflows for AI, finance and operations.
- Its Proof of SQL technology lets recipients verify calculations against trusted data commitments without receiving the underlying database records.
- Businesses can deploy the platform on their own servers, but remain responsible for source-data reliability, access controls and deciding which results to disclose.
Space and Time has launched an enterprise data platform designed to let AI applications and auditors verify calculations without gaining access to the confidential records behind them. The product addresses a difficult trade-off for businesses: using sensitive information in automated workflows while retaining control of the underlying data.
The company introduced Space and Time Enterprise on November 9, naming Maple Finance, Indomobil, Trenez and The Data Foundation as launch partners. Its target uses include AI agents, financial reporting, compliance and business operations.
Space and Time is backed by M12, Microsoft’s venture fund, which led an earlier $20 million funding round. The enterprise launch extends technology previously used in its blockchain data network into private business environments.
Proof Travels With the Answer
The platform uses Proof of SQL, Space and Time’s technology for generating cryptographic evidence that a database query was carried out correctly. A business can share a result and its accompanying proof while keeping the source records on infrastructure it controls.
For example, a lender could seek verification of a collateral calculation without receiving every confidential position in the database. An AI application could check a figure before incorporating it into a workflow. These are potential uses of the system, rather than evidence that every launch partner has deployed the same application.
“Privacy is a prerequisite for enterprise adoption,” said Nate Holiday, chief executive of MakeInfinite Labs, the original developer of Space and Time, in the launch announcement. He argued that verifying the information behind automated decisions becomes more important as businesses give AI agents greater authority to act.
That concern sits alongside the workplace data-leak problem addressed by companies such as Harmonic Security. Preventing an employee from disclosing confidential information and allowing another party to verify a calculation are different controls, but both influence how businesses can safely use private data with AI.
A Private Deployment With Clear Limits
Space and Time says enterprises can deploy the product on their own servers without operating a blockchain, purchasing specialized GPU hardware or sending queries to an external proving service. The company also points to an existing Proof of SQL integration with Google Cloud’s BigQuery. Its SXT Chain network continues to serve onchain finance, including decentralized finance, tokenized assets and stablecoins.
The public repository explains an important boundary: verification establishes that a result matches the submitted SQL and the data represented by a trusted cryptographic commitment. It does not establish that the original records are truthful. Verifiers must obtain commitments they trust independently; accepting both the data commitment and proof from an untrusted operator would leave that gap unresolved.
The repository also says the HTTP interface ships without authentication or authorization, so businesses must provide their own access controls. Query results are returned in readable form, making the choice of what to disclose important even when the underlying database stays private. Cryptographic verification therefore complements a company’s security controls rather than supplying all of them.
The announcement does not disclose enterprise pricing or independently measured customer results. For prospective users, the practical test will be whether the platform can fit existing data systems and audit processes at an acceptable cost. Its proposition is specific: provide checkable evidence for calculations that an AI application or business partner would otherwise have to accept on trust.
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