Which model should we provision?

It depends on your stack, your data posture, and your use cases. We are model-agnostic and will recommend and provision the enterprise LLM, or mix, that fits, often as part of an AI Readiness Audit.

What about governing how it's used?

Provisioning is access; governance is control. LLM Usage Governance adds the acceptable-use policy, DLP, and multi-model framework that keep usage safe and auditable.

Enterprise LLM Foundation

Give your people enterprise AI, on your terms, actually used.

Provision governed enterprise LLM access for employees, ChatGPT Enterprise, Claude, Copilot, or Gemini, with SSO and access control, plus the adoption and enablement that turns licenses into daily, productive use.

Book an initial consultation Start with an AI Readiness Audit

Our people use AI on personal accounts with our data in the prompt. How do we give them a safe, sanctioned option?

Shadow AI is the default when there's no sanctioned tool: employees paste company data into consumer chatbots because it helps them work. This project gives the organization a governed enterprise option, provisioned with SSO and access control, and drives real adoption so people actually move off the unsafe path.

What's included

Enterprise LLM setup

A governed enterprise LLM (ChatGPT Enterprise, Claude, Copilot, or Gemini) set up for your organization, with data handling configured for life sciences.

SSO and access

Single sign-on and access control, so the right people have the right access and provisioning scales cleanly.

Adoption and enablement

Enablement that turns licenses into real use: role-specific guidance, examples, and onboarding so AI becomes part of how the team works.

A sanctioned default

A safe, productive AI option that pulls people off personal accounts and shadow tools.

How it works

1
Provision

We set up the enterprise LLM with the data-handling and access configuration your environment needs.
2
Connect SSO

We wire single sign-on and access control so provisioning scales and stays governed.
3
Drive adoption

We enable the team with role-specific guidance so the licenses get used.

What you walk away with

Governed enterprise LLM access with data handling configured
SSO and access control that scales cleanly
Real adoption, not licenses nobody opens
A sanctioned default that displaces shadow AI

Frequently asked

Which model should we provision?: It depends on your stack, your data posture, and your use cases. We are model-agnostic and will recommend and provision the enterprise LLM, or mix, that fits, often as part of an AI Readiness Audit.
What about governing how it's used?: Provisioning is access; governance is control. LLM Usage Governance adds the acceptable-use policy, DLP, and multi-model framework that keep usage safe and auditable.

Give your team a safe AI to use

Book a consultation to provision and drive adoption of governed enterprise LLM access.

Book an initial consultation Start with an AI Readiness Audit

Where this leads next

LLM Usage Governance

Add the policy, DLP, and multi-model governance that keep AI use safe and auditable.

Explore the project

AI Assistants

Turn governed access into a quick win with role-specific custom GPTs.

Explore the package