One API key. Every model. Every call, enforced.
Not another dashboard that tells you what happened. ThinkNEO decides before the call runs: block what your policy disallows, hard-stop the budget, log every decision. BYOK, five minutes, free to start.
Agentic AI isn't a technology project — it's a governance project.
Enforcement that runs inline — and proves it.
Real runtime behavior, not a test count. Sub-millisecond policy enforcement in the request path, a tamper-evident audit trail you can verify, and fail-closed security that degrades to monitor — so governance is never a single point of failure.
0.825ms p99
Policy enforcement, inline in the request path
SHA-256
Hash-chained, append-only audit trail — tamper-evident and verifiable
0 SPOF
Degrades to monitor on failure — never a single point of failure
| Control | Runtime behavior | What it does |
|---|---|---|
| Inline enforcement | 0.825ms p99 | Policy checks in the request path; fail-closed on security gates |
| Kill switch | Audited | Stop any agent, per-workspace and per-agent — every activation written to the hash-chained audit trail |
| Hash-chained audit | Verifiable | Append-only, SHA-256 chained; tampering detected on re-walk; UPDATE/DELETE blocked at the database |
| Degrade to monitor | No SPOF | On failure or unknown state, controls fall back to monitor — never a single point of failure |
Runtime behavior from the production enforcement path.
Know exactly who uses AI, what each team spends, and what's allowed.
ThinkNEO organizes AI the way your company already works — department, team, and project. Open any project to see the API keys and models plugged in, the agents running on them, and the rules that keep it all safe.
Tap a team or project to explore.
Every team, project, and agent using AI — in plain view.
What each one spends — no surprises at the end of the month.
Decide which keys, models, and agents are allowed — and what's off-limits.
AI Provider Agnostic by Design
Production-ready adapters across leading AI providers: OpenAI • Anthropic • Google Gemini • xAI • Mistral • OpenRouter.
ThinkNEO Governance Layer
Runtime outcomes
"Governance should not depend on your AI vendor.
It should sit above it."
How It Works
Deploy ThinkNEO as an enterprise AI control plane in four operational phases.
Introducing The Enterprise AI Control Plane Pattern
Without ThinkNEO, every AI application talks directly to providers.
With ThinkNEO, every request is inspected, governed, metered, audited, and either allowed or blocked before it runs.
Your AI Apps
ThinkNEO Control Plane
Multi-Provider Models
AI provider agnostic by design — production-ready adapters
Connect applications and providers
Keep existing app flows and provider endpoints while routing through one governed control layer.
Apply policy-aware routing
Route by model, provider, workload, and risk profile with consistent control boundaries.
Enforce runtime guardrails
Run input/output/context/tool controls in monitor mode first, then enforce for active protection.
Operate with observability and FinOps
Use traceability, evidence workflows, and cost-quality analytics to scale with accountability.
Every request is evaluated in the runtime path for routing intent, policy scope, guardrail posture, telemetry capture, and budget impact.
What Enterprises Measure With ThinkNEO
Enterprise teams evaluate governance by operational outcomes that can be reviewed and repeated across security, platform, and finance workflows.
Policy decisions tracked
Each request is evaluated against routing intent, policy scope, and runtime guardrail posture.
Runtime control signal
High-risk requests escalated or blocked
Risk-aware controls run before downstream model and tool execution to reduce unsafe behavior.
Safety and governance signal
Spend anomalies surfaced
Cross-provider telemetry helps finance and platform teams investigate cost drift early.
AI FinOps signal
Investigation timelines reduced
Unified evidence trails shorten handoffs between engineering, security, and compliance.
Operational evidence signal
Representative outcomes shown on this page. References are available under NDA during the enterprise process.
Published, registered, and independently listed.
Public artifacts anyone can open and verify — DOIs, container registries, and program memberships. No numbers you can't click.
Every artifact above resolves to a public, third-party page.
Built for the enterprise you'll become
Yes, our real business is enterprise deployments. That's the point: the $4.99 tier is funded by real revenue, runs the same engine, and won't vanish in six months. Cap your key today; bring us to your platform team when you're ready.
The same control plane that governs your personal key runs compliance and cloud-to-robotics enforcement for teams that need it. You start where you are. It scales when you do.
Point your OpenAI client at ThinkNEO — govern every call in 5 minutes
Runtime enforcement from the cloud down to the robot — BYOK, any model, any cloud. Same engine underneath; you just decide who's responsible.
BYOK · no card · 5 min · p99 0.825ms
▋
Built For Teams Governing AI In Production
ThinkNEO is designed for organizations that need runtime control, accountable operations, and review-ready governance signals across technical and executive stakeholders.
CTO / Head of AI
Needs one control layer across providers without rewriting product stacks.
Unifies governance, runtime controls, and provider strategy in one operational model.
Security & Compliance
Needs traceable policy enforcement and audit-ready evidence under active workloads.
Adds runtime guardrails, evidence trails, and security review workflows.
Platform Engineering
Needs stable provider routing and observability without fragmented tooling.
Standardizes control points for routing, telemetry, and runtime decisions.
Finance / AI FinOps
Needs cost attribution and budget discipline across teams and providers.
Connects spend visibility with policy controls for accountable AI economics.
AI Adoption Without Governance Is Financial Risk
As organizations scale AI usage, costs become unpredictable, attribution becomes unclear, and compliance turns reactive. Multi-provider environments multiply this risk.
The core gap is not model quality alone. It is missing control-plane discipline across runtime safety, observability, governance, and economics.
Costs become unpredictable
Teams create uncontrolled API keys
Attribution becomes unclear
Finance loses visibility
- ✕ No real-time spend attribution
- ✕ No budget enforcement
- ✕ No cost anomaly detection
- ✕ No provider comparison
- ✕ No accountability trail
- ✓ Department-level attribution
- ✓ Hard budget enforcement
- ✓ Model and policy controls
- ✓ Unified provider visibility
- ✓ Immutable audit-ready records
Beyond Proxy, Beyond Gateway, Beyond Point Tooling
ThinkNEO is not a single-function layer. It is the enterprise operating system for AI runtime control, policy accountability, and economic governance in production.
Control Plane, Not Just Routing
One policy surface across providers, models, tools, and workflows.
Runtime Governance
Guardrails, enforcement decisions, and operational response in live traffic.
Economic Accountability
AI FinOps with attribution, budget controls, and spend governance by context.
Enterprise Evidence
Audit-ready records for security, compliance, and executive decision workflows.
ThinkNEO Is The Enterprise AI Control Plane
ThinkNEO extends beyond gateway routing. It provides governed access, runtime safety, deep observability, compliance readiness, agent control, and AI FinOps optimization across models, providers, tools, workflows, and deployment environments.
Positioning Clarity
Not just a proxy, gateway, or cost console
ThinkNEO keeps strong gateway economics while adding runtime controls, operational evidence, and governance workflows required for enterprise AI programs.
A governed control plane for enterprise AI
Policy and economic controls are applied consistently before execution, while traceability and evidence remain available for engineering, finance, security, and compliance teams.
Multi-Provider Usage Normalization
Unified tracking across OpenAI, Anthropic, Google, xAI, Mistral, OpenRouter, and OpenAI-compatible endpoints.
Provider-Specific Adapter Layer
Handles authentication quirks, rate limits, metadata normalization, token accounting differences, and error mapping.
Department-Level Cost Attribution
Tag and assign usage by team, project, workspace, and cost center.
Budget Enforcement
Apply hard caps, soft alerts, and auto-block rules before spend escalates.
Model Policy Controls
Allow or deny specific models per workspace, team, or environment.
Real-Time Monitoring
Live dashboards for finance and operations teams with immediate usage visibility.
Audit Trails
Immutable request logs with timestamp, model, cost, and user metadata.
OpenAI-Compatible Gateway
Drop-in base URL replacement with no provider SDK rewrite required.
Rate Controls & Guardrails
Apply usage guardrails by key, team, and environment to prevent overrun incidents.
Workspace Governance
Tenant-aware isolation with configurable policy and budget boundaries.
Deep-dive modules
Built for Enterprise Governance
Governance without data exposure, designed for enterprise security review.
Tenant Isolation
Isolated tenant architecture with strict workspace boundaries.
Role-Based Access Control (RBAC)
Controlled access by role and workspace-level permissions.
Immutable Usage Logs
Immutable per-request event history for audits and investigations.
Encrypted Key Storage
Secure handling and storage of provider API credentials.
Exportable Audit Reports
Structured governance records ready for finance and compliance workflows.
SIEM-Ready Logs
Streamable event data for enterprise monitoring and detection systems.
SSO-Ready Architecture
Architecture prepared for enterprise identity and access integrations.
SOC 2 Alignment
SOC 2 Type II alignment in progress.
Runtime Guardrails
Input, output, context, and tool-use controls with monitor and enforce operating modes.
Contextual Data Security
Sensitive context controls for contracts, source code, pricing, and internal knowledge paths.
AI Governance Workflows
Operational workflows for risk review, evidence capture, and policy approval accountability.
Agent Action Controls
Governed agent execution with boundaries, approvals, and traceable action records.
Security Posture
ThinkNEO does not train models. Retention of governance metadata is configurable per tenant.
Security, Compliance, And Operational Assurance
Trust signals should be explicit, verifiable, and easy to review by security and procurement teams. ThinkNEO centralizes enterprise-ready controls in one governance layer.
- Tenant isolation and workspace governance boundaries
- Role-based access control and policy-scoped permissions
- Encrypted key handling and controlled provider credential flow
- Audit-ready logs with SIEM-oriented export pathways
- Security review package available on request
- Compliance roadmap with evidence workflows
- SSO-ready and deployment documentation (SaaS, private, hybrid where applicable)
Governance Reduces Waste
AI waste often comes from unrestricted model access, duplicate experimentation, routing inefficiency, and untracked internal usage.
Lower waste exposure
Runtime policies reduce unnecessary model usage and uncontrolled retries.
Better attribution coverage
Spend, policy events, and request metadata remain traceable across teams and providers.
Enforced budget discipline
Budget controls operate in live request paths, not only in retrospective reporting.
Without governance
- ✕Fragmented spend attribution
- ✕Reactive cost controls
- ✕Limited visibility into policy impact
With ThinkNEO governance
- ✓Department-level attribution
- ✓Budget enforcement at runtime
- ✓Unified policy and economic visibility
Illustrative scenario. Actual outcomes depend on policy scope, model mix, and operational baselines.
AI FinOps maturity beyond budget blocking
- Forecasted cost trajectories per workspace, model family, and use case.
- Chargeback and showback support for departmental economic accountability.
- Waste reduction workflows that isolate low-value spend patterns.
- Cost-quality routing strategies that balance reliability, latency, and unit economics.
Integrate ThinkNEO In Minutes
Route enterprise AI traffic through a governed control plane without rewriting your stack. Keep your current SDKs and add policy, observability, and FinOps controls at runtime.Start with your existing OpenAI SDK. Switch the base URL. Keep your app logic.
OpenAI-compatible quickstart
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.THINKNEO_KEY,
baseURL: "https://api.thinkneo.ai/v1",
});
const response = await client.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: "Summarize policy drift risk by provider." }],
});
console.log(response.choices[0]?.message?.content);Control Plane Outcomes By Enterprise Function
See how finance, technology, security, and engineering teams operate governed AI through one shared control plane.
Run AI FinOps With Governance
Move from basic spend tracking to forecasting, chargeback readiness, risk visibility, and policy-aware economic control.
Get started- Real-time AI cost visibility
- Forecasting inputs by tenant, workspace, and workload
- Department-level and business-unit attribution
- Enforced budgets (blocking, not just alerts)
- Chargeback/showback-ready reporting
- Reduced waste and surprise spending through runtime controls
- Audit-ready economic evidence for finance and compliance
Your Apps -> ThinkNEO Governance Layer -> AI Providers
Routes requests
Tracks real usage
Enforces budgets and policies
ThinkNEO acts as an OpenAI-compatible gateway that normalizes provider metadata and generates audit-ready logs.
The same layer governs models, providers, tools, agent workflows, and deployment boundaries with one consistent policy surface.
Frequently Asked Questions
Everything you need to know before getting started.
Adopt Governed AI With Runtime Control
Keep your current provider stack and applications. Add ThinkNEO as the control plane for runtime safety, observability, compliance readiness, and AI FinOps.
Built for enterprise teams that need accountable AI operations across models, tools, workflows, and deployment boundaries.


