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Enterprise AI Governance · Design, integration, and rollout

Keep your AI tools. Bring the governance.

Super Amplify helps enterprises connect their preferred AI tools to centrally managed identity, models, company knowledge, and business systems. Give teams room to work, while keeping policies, approvals, audit trails, and cost controls close to every governed request.
Explore the service
Tool choiceCentral governancePhased rollout
Explore the broader enterprise platform

Familiar tools. A governed connection.

  1. Approved AI tools

    • Claude Code
    • Codex
    • Cursor
    • Cowork

  2. Super Amplify

    Company identity, policies, approved context, and oversight.

    AI gatewayModel access + routing

    MCP / tool gatewayScoped knowledge + actions

  3. Approved models

    Provider choice within policy

    Company knowledge + tools

    Only the permitted context and actions

Reference architecture, not a live connection. Client compatibility and control coverage are confirmed for your deployment.

What we deliver

One enterprise standard. Across your AI stack.

A service for IT, security, engineering, and business leaders who want AI adoption without a patchwork of unmanaged connections. We design and configure the controls with your team, then help make them part of day-to-day work.

Identity and policy, managed centrally.

Give people the capabilities their role needs, with a shared policy framework across teams and projects.

  • Company SSO, MFA, and scoped OIDC / SAML integration
  • Group and role policies, provisioning, and access revocation
  • Data classification, DLP rules, and approved destinations

Model choice. Visible spend.

Connect approved providers through a central AI gateway, rather than managing a separate model setup on every desktop.

  • Provider and model allowlists, routing, and fallback policies
  • Token budgets, rate limits, and cost controls
  • Usage, cost, and performance reporting by team or project

Company knowledge that stays yours.

Keep knowledge and permissions in a managed context layer, so changing tools does not mean rebuilding your organization's memory.

  • Scoped retrieval from collections, documents, and organization memory
  • Separate, permissioned personal and company context
  • Source traceability, access records, retention, and deletion rules

Connected tools. Clear approvals.

Let approved agents search knowledge, use business systems, and trigger workflows within an explicit permission boundary.

  • Managed MCP servers or equivalent tool APIs
  • Authorization and logging for governed tool calls
  • Human approval for sensitive actions and scoped execution policies

An audit trail your team can use.

Make governed activity attributable and reviewable, with capture and retention settings aligned to your data-handling requirements.

  • Policy-defined prompt, response, tool-call, and token records
  • Redaction, retention, archiving, and evidence exports
  • Event delivery to SIEM, analytics, and downstream workflows

One rollout. Less desktop sprawl.

Configure the operating model centrally and give users a lightweight connection path into their approved tools.

  • Admin catalogs for platforms, models, context, and tool policies
  • Scoped SCIM provisioning and MDM-managed configuration
  • Guided connection or bootstrap flows, with short-lived credentials where supported

How the connection works

The workspace stays familiar. The control layer stays yours.

Use complementary integration paths, rather than making one desktop extension or webhook responsible for the whole operating model.

  1. 01

    Central AI gateway

    The live model-request path checks identity, policy, model permissions, and budgets; supplies approved context; routes to an approved provider; and records governed activity.

  2. 02

    MCP and tool gateway

    Company knowledge and business actions are exposed through permission-aware retrieval and approved tools. Sensitive actions can require human approval before execution.

  3. 03

    Native integrations where useful

    Configuration packages, connectors, or extensions make supported clients easier to use. They improve the experience without becoming the only place governance lives.

Gateways enforce. Webhooks report.

Live authorization, model routing, streaming, and tool results belong in synchronous gateways or APIs. Events can then flow to archives, SIEM, analytics, and downstream workflows. An event notification is not a substitute for blocking an unauthorized request.

Open by design. Bounded by policy.

Choice is not the same as unrestricted access.

Each connection is assessed against your identity, data-handling, audit, revocation, and execution requirements. Access follows the controls the client and your environment can actually support.

Super Amplify’s governed layer

  • Identity, model allowlists, approved context, and tool permissions
  • Policy checks and approvals on managed request paths
  • Governed activity records, retention, archiving, and usage visibility

Client and endpoint controls

  • The user interface, editor, and local project experience
  • Local files, execution, secret scanning, and repository controls
  • Device management, network egress, and prevention of unmanaged bypass paths

  • Approved access

    Fully governed

    Clients that meet the agreed identity, gateway, tool, audit, and data-handling requirements can receive their approved model, context, and tool access.

  • Restricted access

    Partially governed

    Where control coverage is limited, restrict use to non-sensitive projects, exclude confidential source code and organization memory, and limit available tools.

  • No company data

    Unmanaged

    Clients that cannot meet minimum controls are blocked from company data and enterprise tools. Tool choice does not mean unrestricted access.

Supported clients, versions, plans, identity protocols, SCIM provisioning, and deployment methods are confirmed during scoping. Product names identify their respective owners, not an endorsement or partnership.

From pilot to operating model

Start with one connection. Scale what you can verify.

A phased rollout makes the boundaries and evidence clear before extending access across more teams and platforms.

  1. 01

    Define the boundary

    Review your AI stack, identity provider, data classifications, devices, and security requirements. Agree platform compatibility, control ownership, and a pilot success scorecard.

  2. 02

    Prove one governed connection

    Start with one team and one approved platform. Configure identity, model access, the gateway, and audit events, then verify onboarding and revocation.

  3. 03

    Connect context and actions

    Add scoped company knowledge, managed tools, and approval workflows. Test the permitted paths and the actions that should be denied.

  4. 04

    Expand with evidence

    Extend to more teams and approved clients. Add the agreed provisioning, device management, SIEM, retention, and departmental controls, and review usage and cost.

Agree what success looks like.

Before expanding, verify that users can connect through company identity, receive only permitted capabilities, and leave an attributable record for governed requests under the agreed archival policy.

  • Onboarding effort
  • Governed request coverage
  • Approval and archive evidence
  • Time to revoke access
  • Usage and cost by team

Before your rollout

Practical questions. Clear boundaries.

Review our Trust Center
Do we have to replace the AI tools our teams already use?

Not necessarily. The service is designed to preserve familiar client experiences while connecting approved capabilities to a governed enterprise layer. Claude Code, Codex, Cursor, Cowork, and other tools can be assessed for your environment. Supported features, versions, plans, and integration depth are confirmed during scoping; these names are not a promise of universal, prebuilt connectors.

Does an approved tool get access to all our company knowledge?

No. Organization knowledge is exposed through scoped, permission-aware retrieval. Personal and company context remain separate and permissioned. The governed request receives the context needed for the task, rather than a blanket copy of your organization's knowledge.

What does the gateway control, and what stays on the endpoint?

Central controls apply to requests and tools routed through the managed services. The client still owns its interface and local editor experience. Local files, execution, and direct-provider or other bypass paths also require client, endpoint, and network policies. A gateway or event webhook alone does not control every action on a desktop.

How are logging, retention, and provider data use handled?

We agree what is captured, redacted, retained, archived, and exported for your deployment. Provider and client data-use terms, including model training restrictions, are part of the approval process. Logging can include prompts, responses, tools, and usage under that policy; it is not a blanket promise to capture sensitive content or retain everything indefinitely.

How do we get started?

Bring your current AI tools, team structure, identity setup, and governance priorities. We will help define a focused pilot, its integration scope, the control owners, and the evidence needed before expanding. Delivery scope and commercial terms are agreed for your organization.

Make your next step concrete

Bring your AI stack. Let’s define your rollout.

Tell us which tools your teams use, what needs to stay protected, and where governance is getting fragmented. We will help scope a connected enterprise environment and a practical first pilot.