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Zynetra

Your AI estate.
In plain sight.

Your AI estate keeps growing.
Your visibility should too.

Discover the AI you know about—and the AI you don’t. Connect agents, owners, costs, and policies in one shared view of your enterprise.

See how it works
YOUR AI ESTATE. ONE CONNECTED PERSPECTIVE.A clearer perspective

The register looks complete.

Your inventory shows the AI your teams have registered. But a tidy list is only a view of what is known, not proof that every workload is accounted for.

Bring hidden AI into view.

Nucleus’s proposed discovery approach connects endpoint, cloud, identity, and gateway signals to surface AI workloads. Coverage depends on the sources available; unknown areas remain visible.

Every AI workload.
One clear picture.

Explore the connections between AI discovery, accountable owners, costs, and governance.

Discovery & signals

Surface AI activity across endpoints, cloud infrastructure, and SaaS sources.

Local modelsShadow AI

Inventory & ownership

Connect each workload to an owner, business purpose, and deployment context.

Accountable ownersAI registry

Cost & attribution

Bring agent usage and business-unit context together. Keep unknown costs visible.

Usage contextCost attribution

Policy & governance

Route exceptions to the responsible team and preserve an inspectable review record.

Policy reviewsAudit history

Five layers of clarity.
One control plane.

Discovery, inventory, cost, governance, and intelligence. Each layer adds context to the next.

Discovery

Bring AI activity beyond the existing register into view.

  • Connect endpoint, cloud, identity, and gateway signals.
  • Surface local models and self-hosted workloads.
  • Keep coverage gaps visible when sources are unavailable.
Clarity across your AI estate. Blue bars represent disconnected records and a rising cyan line represents connected Nucleus context across Discovery, Ownership, Cost, and Policy. Illustrative concept, not measured coverage.
Illustrative concept · product preview, not measured coverage

Less searching.
More visibility.

Your AI estate shouldn’t be a collection of disconnected records. Nucleus brings discovery, ownership, cost, and policy context into one control-plane vision.

Connected workloads. Accountable owners. A clear next step for each team.

See how Nucleus works
A connected AI estate illustrates shared visibility across enterprise workloads.

Your whole AI estate.
One clear perspective.

Bring workloads, ownership, costs, and review states into a connected view. Give each team the context to decide what comes next.

  • Connect AI workloads with accountable owners
  • Make unattributed costs visible
  • Keep policy reviews connected to the record

Look beyond
the cloud console.

Four discovery blind spots shape the Nucleus approach. Each needs a different signal—not just another list of cloud resources.

A local AI runtime is inspected on a laptop even though its external network cable is disconnected.

Local models

A model running on a developer laptop may never make an external API call.

Proposed signal: endpoint process activity
A cloud server enclosure opens to reveal the inference engine inside ordinary infrastructure.

Self-hosted inference

A cloud VM can look like ordinary infrastructure while serving a model.

Proposed signal: runtime and infrastructure context
A service identity and AI access key are inspected before a dormant compute capsule is activated.

Pre-deployment credentials

An identity with access to AI credentials may exist before its first deployment.

Proposed signal: identity and credential metadata
An identity record connects activity inside an enterprise boundary to an external AI service.

Shadow SaaS AI

AI activity can happen outside the infrastructure your teams operate.

Proposed signal: identity and access records
An AI application follows approved model, gateway, and logging checkpoints into an owned deployment.

Make the approved path
the useful path.

Golden Paths are the proposed developer experience: reusable, approved deployment templates that bring the right model, gateway, logging, and review requirements into the starting point.

  • Start from an approved deployment pattern
  • Keep ownership and required controls explicit
  • Connect the release to its governance record

Connected intelligence.
Your governance.

A policy exception in an AI audit record is connected to its responsible owner.

Accountability comes first.

Understand the discovered workload, its data access, and the policy questions that need an accountable review.

Three AI workloads route their usage costs into three separate ownership trays.

Costs stay in view.

Connect agent usage to owners and business units. Keep unattributed or unavailable costs visible.

Approved model, gateway, logging, and ownership components assemble into a deployment-ready AI module.

An approved path forward.

Give developers approved starting points and a clear path from registration to deployment review.

A clear vision.
An honest roadmap.

The March 2026 product preview includes interfaces for inventory, shadow discovery, topology, governance, FinOps, insights, connectors, and Golden Paths, using representative mock data.

Live capabilities are being developed in phases. Discovery, integrations, cost attribution, and enforcement are part of the product roadmap. The copilot is planned for a later phase.

This page demonstrates the intended product experience. Confirm current availability, source coverage, access requirements, and pilot scope with the team.

Good questions.
Clear answers.

What is Nucleus?

Nucleus is the Zynetra AI control-plane concept. It brings discovery, inventory, agent-level cost attribution, governance, and insights into a shared view of enterprise AI. The product is in preview, with live capabilities on a phased roadmap.

How is this different from an AI gateway?

A gateway can provide visibility into traffic routed through it. Nucleus’s proposed approach also uses cloud, endpoint, and identity signals to find workloads outside that path, then connects them to ownership, cost, and governance context.

Will Nucleus discover every AI workload?

Coverage depends on the sources connected, permissions granted, and signals available. Local models, self-hosted inference, and SaaS usage require different discovery methods. An absence of signals does not prove that no AI exists.

Does the preview enforce policies or connect to my cloud?

No. The examples on this page use illustrative records. Live connectors and policy enforcement are on a phased roadmap. A walkthrough should establish which capabilities are available for your environment.

Does it guarantee compliance?

No. Inventory, policies, approvals, and audit records can support your governance work. Your organisation still needs to establish its requirements, assess the evidence, and make its own compliance decisions.

Give your AI a centre.
With Nucleus.

Explore the control-plane vision and shape a deployment around your organisation.