Model & agent registry
You cannot govern AI you have not counted. The registry inventories every model, agent, and prompt across your systems as a governed asset, each with a named owner, a stated purpose, and a risk tier, on the same knowledge graph that already holds your data. When an examiner asks for your model inventory, you point at the graph and the evidence behind every entry.
One inventory covers every model, agent, and prompt, and each entry carries a named owner, a stated purpose, and a risk tier.
Connector-based discovery reconciles the inventory against deployment evidence, so an unregistered model surfaces as a finding rather than staying invisible.
Risk tiers are derived from what the asset actually touches, including customer data, regulatory numbers, and money movement, and they are never set by hand.
Imported inventories land as claims and are checked against deployment evidence, and an entry with nothing behind it shows as unverified.
Lineage links connect each model to the CDEs and data sets that feed it, on the same graph as the rest of your governance.
Every registration, edit, and retirement is sealed on the hash-chained ledger.
Discover
Connectors scan your systems for deployed models, agents, and prompts. They read metadata only, and nothing is copied.
Register
Each asset lands on the knowledge graph with an owner, a purpose, and a derived risk tier.
Reconcile
Existing inventory records import as claims and are checked against evidence, so gaps and orphans surface as findings.
Seal
Every change to the inventory is sealed on the hash-chained ledger.
“How many models do we actually run, and who owns each one?”
The registry gives you one inventory of every deployed model, agent, and prompt, discovered through connectors and reconciled against deployment evidence. Each entry carries a named owner, a stated purpose, and a risk tier, so the answer is a lookup rather than a project.
“Is this use case in scope for SR 11-7 or the EU AI Act?”
Every asset carries a risk tier derived from what it actually touches, such as customer data, regulatory numbers, or money movement. That tier, together with the stated purpose and the lineage to the data that feeds the model, tells you exactly where the use case sits against SR 11-7 and the EU AI Act.
“Can you prove your model inventory is complete?”
Yes. Connector-based discovery reconciles what is actually deployed against what is registered, imported records are checked as claims against deployment evidence, and every change is sealed on the hash-chained ledger. Anything without evidence behind it shows as unverified rather than being hidden.
Start with proof, not a promise.
There are three ways in, and each one earns the next. You approve the exit criteria before we spend any more of your time or budget.
A 60-minute working session
Bring one report that is expensive to prove. We walk the mechanism against it. Nothing is required from your side.
A two-week guided sandbox
Your team works hands-on against a synthetic sample bank. It requires zero access to your systems and zero procurement.
A six-week proof on your systems
We use read-only metadata across five systems and one priority report, scored against criteria both sides sign first. The decision it produces is yours.