How ThinkNEO Operates As The Enterprise AI Control Plane
ThinkNEO sits between applications and AI providers to enforce policy, capture telemetry, and govern economics without forcing a full stack rewrite.
- Apps and workflows call a unified OpenAI-compatible control layer.
- Policy controls run at runtime for routing, limits, and guardrail outcomes.
- Operational evidence is captured for engineering, security, and finance.
Request Flow
The production path is designed for governed execution, provider flexibility, and accountable operations.
Control Plane Responsibilities
ThinkNEO centralizes runtime controls so enterprise teams can govern AI behavior across providers and workloads with one operating model.
- Policy-aware routing and provider governance
- Runtime guardrails for input, output, context, and tool use
- Budget controls and economic thresholds by scope
- Usage attribution by tenant, workspace, project, and owner
- Audit-oriented operational records
Telemetry and Audit
Operational visibility is built for investigation speed and governance accountability, not only dashboard cosmetics.
- Request-level and stage-aware telemetry
- Outcome-linked policy and guardrail context
- Export-ready evidence for compliance and review workflows
- SIEM-oriented integration pathways
Workspace Isolation and Boundary Controls
The architecture supports enterprise boundaries for teams, projects, and key ownership to reduce operational risk.
- Role-scoped access boundaries
- Workspace and project policy segmentation
- Controlled provider credential handling
- Separation of operational and governance contexts
Review Architecture With Your Platform Team
Use this architecture baseline to run technical due diligence with security, platform engineering, and AI operations stakeholders.