Embed AI OS

The control layer for AI inside your enterprise

One operating layer between the systems you already run and the agents, assistants and AI tools working on top of them.

In 30 minutes, we define your first use case with a clear ROI.

The standard

The layer that outlives the next model is the one that matters.

Context, tools, policy and evaluations live in the platform rather than inside an agent. Changing model does not mean rebuilding the operation.

Meet the agents

The difference

The alternative to a shared layer is rebuilding it in every project.

Without a shared layer

Every use case solves identity, permissions and integration again.
Credentials end up spread across teams and tools.
Nobody has a single view of what is used, what it costs and at what risk.
What one project learns never reaches the next one.

With Embed AI OS

Identity, catalog and policy are defined once.
Credentials are issued temporarily and never exposed to the model.
Usage, cost, quality and outcome are measured by team and by workflow.
Every published tool and skill accelerates the next deployment.

Control at runtime

Every action passes through control before it touches a system

The layer evaluates who is requesting the action, which tool is being invoked, with which arguments and against which resource. What policy allows runs with a temporary credential; what requires judgment stops and waits for approval.

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Policy is evaluated by person, role, team and resource.
Human approval is requested where it adds control, not at every step.
Credentials expire with the task and are never shared with the model.
The trace stays tied to the identity that originated the request.

Security

The agent only sees and does what your company allows.

More about security

Approves when it is safe. Escalates when it is not.

The agent handles routine work inside your policy. Anything sensitive or ambiguous goes to a person, with full context.

Answers on WhatsApp, inside your policy.

Customers write on the channel they already use. The agent offers only what your policy allows and records every agreement.

Every action is checked before it touches a system.
Before anything runs, the tool and its parameters are validated. Sensitive actions wait for human approval, and everything is recorded.
What one team builds stays available to the next.
We measure adoption, cost and outcome, and publish agents and tools as reusable pieces.
The agent works with the real context of the company.
We connect CRM, ERP, data warehouse, documents and APIs, and publish them as approved tools the agent can use.

What the layer governs

What stops being solved case by case

Identity and permissions
Separates who may use an agent from what that agent may read or execute, by person, role, team and resource.
Tool catalog
Publishes APIs, databases, documents and procedures as versioned tools with a defined owner and scope.
Governed execution
Validates every call and its arguments before it touches a system, and asks for human review where it belongs.
Evaluation
Tests each version against real cases before release and keeps measuring quality afterwards.
Observability
Ties consumption, latency, cost and outcome to specific teams, agents and processes.
Distribution
Delivers the same capability to the channels your people already use, without integrating it again in each one.

The compounding advantage

Every deployment leaves more foundation for the next one

The layer turns isolated work into shared capability: an integration built once, a policy written once, a skill tested once.

Reuse
Published tools and skills stay available, with version and permissions, to the rest of the organization.
Control
Security, IT and the business work on one architecture with explicit responsibilities.
Measurement
Adoption stops being an impression: it is visible by team, by workflow and by outcome.
Model optionality
What you build is not tied to one provider: the model is chosen per task and can be changed.