Connect
Connect model APIs, enterprise applications and tools that require integration.
Enterprise AI Control Plane
Run enterprise models and agents under shared rules.
Organize access, identity, policy, observability and optimization in one governance framework to understand and manage enterprise AI usage.
Built for enterprise
01
Usage
Who is using which models and agents?
02
Boundaries
What data can they access, and what actions can they take?
03
Investment and outcomes
What resources were spent, and what work was completed?
01 / Architecture
Give access, control and evaluation clear roles.
02 / Four core capabilities
Start with connection. Keep improving enterprise AI.
Link fragmented integrations, policies and records so managers can understand investment and outcomes for the same task.
Control
Identity, model permissions, budgets, data and execution policies.
Observe
Records of users, applications, tasks, model calls and exceptions.
Optimize
Choose models by task difficulty and refine workflows using quality and outcome feedback.
03 / Control and data boundaries
Know where policies and business data go.
Define where policies, requests, credentials and logs are handled for each deployment, with an explicit boundary for every connection.
Control plane
Manage configuration and policy; define required metadata and synchronization in the solution design.
Data plane
Parse and forward requests, with deployment planned for an intranet, VPC or agreed environment.
Credentials and logs
Define storage, access, rotation and content-retention scope.
External calls
Public-cloud models receive approved content. Bringing your own key does not make content invisible to intermediaries.
04 / Governance across a task
Apply controls where actions actually happen.
Follow an enterprise agent task from identity checks to outcome evaluation to see where each policy applies.
1 Identity and data
Confirm task identity, data scope and permitted outbound content.
2 Model selection
Match difficulty, quality and budget within the allowed model set.
3 Tool execution
After the model proposes an action, the execution endpoint checks parameters, permissions and approval.
4 Results and evaluation
Record outputs, human intervention and failure reasons, then review against business criteria.
05 / Continuous improvement
Give every policy change a basis.
Turn resource tests and task evaluations into recommendations. Review, test and retest before updating production settings.
Observe
Collect quality, cost, latency, errors and task outcomes.
Recommend
Identify oversized models, repeated steps and quality gaps.
Validate
Compare options on the same test set and expand only after meeting the quality threshold.
Roll back
Keep policy versions and rollback paths for issues introduced by new settings.
06 / Integration and next steps
Start with what your enterprise needs most.
Reuse existing models, applications and identity systems. Validate one scenario or governance capability, then expand.
Start with business work
Validate outcomes with X Worker and a digital workforce scenario.
Start with model access
Improve model usage through unified access and routing.
Start with security
Define sensitive-data and tool-execution boundaries, then integrate control points.