Enterprise AI Gateway & Control Plane
The control plane for every AI request.
Connect private and public AI models behind one governed workspace and one OpenAI-compatible API. Tensor Cortex decides which models each user, application, and data class can use before optimizing for cost or speed.
- Customer-hosted
- Single-tenant
- Bring your own model keys
- Metadata-only audit by default
req_tc_8d4a corp-glm-primaryselectedhealthy · private · eupublic-reasoningrejectedpublic trust zone01 / Platform
Private and public AI, governed as one system.
Tensor Cortex turns identity, application context, data sensitivity, model capabilities, health, and budget into one explainable route decision. Security filters the candidate set first. Optimization only happens inside that set.
Workspace
One AI workspace. No provider decisions for employees.
Employees use company-published profiles such as Automatic, Private, Fast, Coding, or Finance. Tensor Cortex applies company policy in the background and shows a clear security classification on every response.
- OIDC single sign-onTeams and roles follow corporate identity.
- Privacy can only move upUsers may request Private mode, never bypass policy.
- History follows policyConversation storage can be disabled or encrypted with retention.
Gateway
One stable API while models and providers change.
Applications connect to logical profiles, not physical model IDs. Move the
code or automatic profile to a different deployment without
shipping application changes.
- OpenAI-compatibleChange the base URL and API key.
- Streaming and tool callsNormalized across supported providers.
- Scoped service accountsApplication identity, profile access, and trusted metadata.
from openai import OpenAI
client = OpenAI(
base_url="https://ai.company.com/v1",
api_key="tc_live_..."
)
response = client.chat.completions.create(
model="automatic",
messages=[{
"role": "user",
"content": "Summarize this incident."
}]
)Control
Model access, routing, audit, and spend in one control plane.
Publish policy without changing application code. Test a user, team, profile, and data class before release, then inspect every selected and rejected deployment through the route decision record.
- Versioned policyDraft, simulate, publish, and roll back.
- Deployment healthActive and passive signals with circuit protection.
- Usage and budgetsAttribute tokens and estimated cost to teams and apps.
WHEN
THEN
SIMULATION
- Profile
- Automatic
- Result
- Allowed
- Selected
- corp-glm-primary
- Rejected
- 2 deployments
02 / Security invariant
A model failure never becomes a data boundary failure.
Fallback is evaluated against the same policy-approved deployment set. Once a stream starts, Tensor Cortex never silently continues the answer from another provider.
Review the security architectureBuilt for governed use
Enterprise controls without a platform team-sized build.
03 / Deployment
Your AI control plane runs in your trust boundary.
Each customer receives a dedicated, single-tenant Tensor Cortex deployment. Provider credentials, route metadata, and optional conversation history stay inside that environment.
04 / FAQ
Enterprise AI gateway questions, answered.
Technical and deployment questions teams ask before standardizing AI access.
What is Tensor Cortex?
Tensor Cortex is an enterprise AI access and control plane. It gives employees one governed AI workspace, applications one OpenAI-compatible gateway, and IT and security teams one place to manage model access, routing policy, safe fallback, audit, usage, and budgets.
How is Tensor Cortex different from a basic LLM gateway?
A basic LLM gateway proxies requests or selects a provider. Tensor Cortex evaluates who is making the request, which application it belongs to, the data class involved, and the active company policy before it considers cost or latency. The same policy system governs both the employee workspace and developer API.
Can it route between private and public AI models?
Yes. Tensor Cortex connects private OpenAI-compatible endpoints, models in a customer VPC or on-prem environment, enterprise cloud endpoints, and approved public providers. Logical profiles such as Automatic, Private, Fast, or Coding hide physical model changes from users and applications.
What happens when a private model is unavailable?
Fallback stays inside the policy-approved trust boundary. If a confidential request is private-only and no compliant private deployment is available, Tensor Cortex returns a safe error and records the route decision. It does not silently send the prompt to a public provider.
Is the gateway compatible with the OpenAI API?
Tensor Cortex exposes an OpenAI-compatible chat completions endpoint with streaming and logical model aliases. Existing applications can normally connect by changing the base URL and API key while keeping a stable model name such as automatic or code.
Where is Tensor Cortex deployed?
The first production model is single-tenant. Tensor Cortex can run in your Kubernetes environment, cloud account, VPC, or on-prem network. A dedicated managed instance is also available. Docker Compose supports focused pilots.
Does Tensor Cortex store prompts and responses?
Audit is metadata-only by default. Prompt and response content is not written to audit logs unless an organization explicitly enables encrypted content retention. Workspace conversation history can also be disabled by policy.
Does Tensor Cortex deploy or train models?
Tensor Cortex is the control layer, not an inference engine or GPU scheduler. It connects to model endpoints you already operate or procure. Private model deployment and managed inference can be delivered separately from the core platform.
Tensor Cortex
One workspace. One API. One policy for every model.
See how Tensor Cortex would connect your private endpoints, public providers, identity system, and data policies.
Book a technical demo