The enterprise control layer for AI.
Nucleus sits between AI clients and your enterprise systems. Before an AI-initiated action executes, it establishes who is asking, applies policy, routes approvals, controls what data leaves, and records the decision and its cost.
Every AI-initiated action takes the same governed path
AI clients don't call enterprise systems directly. They ask Nucleus, which applies your controls and then hands the request to the system that owns it.
- 01 · Request
An assistant, agent or automation asks to perform a capability against a system, such as reading a sales order in SAP.
- 02 · Identify
Nucleus establishes the person, the AI client and the delegation chain behind the request.
- 03 · Decide
Policy is evaluated, approvals are routed where required, and data controls are applied.
- 04 · Execute
The request is routed through a connector. The downstream system applies its own authorization.
- 05 · Record
The decision, the outcome and the cost are written to a signed, append-only record.
Seven controls, applied before execution
- Identity
- Every request tied to a person, an AI client and a delegation chain
- Policy
- Rules evaluated per capability, system and role
- Approvals
- Human sign-off where the action warrants it
- Routing
- The right model and connector for each request
- Data controls
- Egress rules on what leaves, and to which model
- Audit
- An append-only, signed record of every decision
- Cost
- Spend attributed by team, client and use case
Built to be reviewed by your security team
Nucleus adds a control point. It does not take authority away from the systems you already trust.
Downstream authority is preserved
Nucleus decides whether an AI action may be requested. SAP, Salesforce and every other system still decide whether it is allowed.
Identity end to end
Every request carries the person, the AI client and the delegation chain behind it.
Tamper-evident audit
Request traces are signed, so the record of what was asked, decided and executed can be verified.
Data egress controls
Rules on what data may leave, and to which model, apply before a prompt is sent.
Kill switches
AI-initiated actions can be stopped immediately when something looks wrong.
Evaluation before exposure
Policies and models are tested in evaluation runs that never touch production systems.
One place to operate, govern and observe AI actions
Requests, governance, security, observability, cost and build tools sit in one console, organized around the same requests.
- Requests
- Approvals
- Connectors
- Capabilities
- AI clients
- Policies
- Kill switches
- Identity
- Data egress
- Posture
- LLM traces
- Evaluations
- Audit log
- FinOps
- Models & routing
- Engineering
- Flow
- Playground
- Test console