Enterprise AI Control Plane For Governed AI
ThinkNEO combines gateway control, runtime enforcement, observability depth, compliance readiness, agent governance, and AI FinOps in one operational platform.
- Governed access across models, providers, tools, workflows, and deployment boundaries.
- Runtime policy controls with monitor and enforce modes before execution.
- Operational evidence for finance, security, engineering, and compliance teams.
White Paper Capability Architecture
ThinkNEO is designed as an additive enterprise layer: keep your provider flexibility, add governance discipline, and scale operations with accountable controls.
Enterprise AI Gateway
Provider abstraction and policy-aware routing through OpenAI-compatible integration patterns.
Deep Observability
Request, session, workflow, and agent telemetry with operational traceability.
Runtime Guardrails
Input, output, context, and tool-use controls with staged enforcement behavior.
Contextual Data Security
AI-specific controls for sensitive business data, internal knowledge, and oversharing prevention.
Regulatory Governance
Support for inventory, ownership, risk classification, and approval trail workflows.
Agent Lifecycle Governance
Agent and tool governance with approval boundaries and auditable execution context.
Governed Connectors & Actions
Permission-aware connectors and controlled enterprise actions with delegated authorization.
PromptOps & EvalOps
Prompt lifecycle, evaluation workflows, regression checks, and release confidence workflows.
Enterprise Identity & Security
SSO-ready architecture, role controls, tenant isolation, and secure key boundaries.
AI FinOps
Forecasting, chargeback/showback readiness, waste reduction, and cost-quality routing optimization.
Control Plane Coverage Matrix
ThinkNEO applies one governed operating model across the full AI execution surface, not only at gateway ingress.
- Model layer: approved model families, policy constraints, and staged deprecation controls.
- Provider layer: routing precedence, fallback paths, and cost-quality decision policies.
- Tool layer: governed connectors with permission checks and just-in-time authorization hooks.
- Workflow layer: stage-level policy checkpoints from intake to post-processing and action execution.
- Agent layer: registry-based ownership, autonomy limits, and kill-switch pathways.
- Deployment layer: boundary policy across cloud, private, hybrid, and enterprise-managed environments.
Deep Observability
ThinkNEO observability is designed for operational diagnosis, governance review, and economic accountability across the AI execution chain.
- End-to-end traces spanning request, session, workflow, and agent execution timelines.
- Stage-level spans for routing, policy checks, tool calls, model execution, and post-processing.
- Replay and debug support with policy outcome context and safety-aware payload handling.
- Scorecards for quality, latency, policy outcomes, and spend by tenant, app, model, and use case.
- Cross-functional visibility for engineering, platform operations, security, and finance teams.
Runtime Guardrails
Policy controls can run in monitor mode for rollout confidence or enforce mode for active runtime intervention.
- Input guardrails for prompt risk signals, data handling violations, and policy non-compliance.
- Output guardrails for unsafe generations, leakage risk, and regulated response controls.
- Context guardrails to validate retrieval payloads, sensitivity class, and residency boundaries.
- Tool-use guardrails with allowlists, permission checks, parameter validation, and egress controls.
- Prompt injection and jailbreak defense with risk scoring tied to enforceable policies.
- Exfiltration prevention controls for outbound actions, response channels, and connector operations.
Contextual Data Security
ThinkNEO data controls are designed for enterprise knowledge risk, not only generic PII filtering.
- Sensitive-data classification support for contracts, source code, pricing, internal docs, and strategic IP.
- Masking, tokenization, and redaction controls across prompts, retrieval context, and generated outputs.
- AI-specific DLP policies for prompt input, model output, and governed tool action payloads.
- Data residency and boundary policy support by tenant, workspace, region, and execution path.
- Oversharing prevention controls for internal knowledge and external communication surfaces.
- Lineage visibility from source context to prompt, model decision, output, and resulting enterprise action.
Regulatory Governance And AI GRC Readiness
ThinkNEO is designed to support enterprise AI governance structures and stricter regulatory environments.
- AI inventory support with system ownership, purpose classification, and operational status.
- Risk classification workflows and impact assessment checkpoints for higher-risk AI applications.
- Approval trails for policy changes, deployment decisions, and operational control exceptions.
- Evidence workflows for internal audit, governance committees, and external review readiness.
- Exportable records supporting alignment with enterprise control frameworks.
- Claim-safe positioning: supports readiness and alignment, without overstating certification status.
Agent Lifecycle Governance
Agent governance in ThinkNEO is structured to manage capability growth without losing operational control.
- Agent registry and tool registry to maintain ownership, purpose, and execution boundaries.
- Approval workflows for new agents, tool connections, and significant capability changes.
- Human-in-the-loop gates for high-risk actions and sensitive enterprise workflows.
- Autonomy limits and permission boundaries scoped by role, workspace, and policy context.
- Kill-switch and suspension pathways for incident containment and controlled rollback.
- Auditable records of decisions, tool calls, outputs, and operator interventions.
Enterprise Identity, Deployment, And AI FinOps
Identity and deployment flexibility remain core for enterprise adoption, while FinOps expands from spend visibility to optimization maturity.
- SSO-ready posture for enterprise identity integration patterns including SAML and OIDC paths.
- Role and boundary controls for workspace governance and tenant-level separation.
- Deployment pathways to support private, hybrid, and enterprise-managed operating models.
- FinOps workflows for forecasting, chargeback/showback, and waste reduction operations.
- Cost-quality routing posture to support reliable outcomes with accountable economics.
Operating Model Across Enterprise Functions
The control plane is designed to support shared operating discipline across finance, security, engineering, and governance teams.
- Finance: AI FinOps scorecards, forecasting inputs, and policy-aware economic reporting.
- Security: runtime threat controls, data-boundary enforcement, and incident-ready evidence trails.
- Platform engineering: routing governance, policy rollout controls, and reliability diagnostics.
- Risk and compliance: inventory, classification, approval records, and audit-ready operational evidence.
- Application teams: safer prompt and workflow iteration inside governed release boundaries.
Module Deep Dives
Explore public deep-dive documentation for core control-plane modules.
Deep Observability
Tracing, stage-level spans, scorecards, replay workflows, and runtime diagnostics.
Runtime Guardrails
Policy enforcement modes, injection defense, jailbreak defense, and exfiltration prevention.
Governance & Compliance
AI inventory, risk classification, approval trails, evidence workflows, and audit readiness.
Adopt Governed AI Without Replacing Your Stack
Keep your providers and existing application flows. Add ThinkNEO as the enterprise AI control plane to govern runtime behavior, evidence quality, and economic outcomes.