AI-CHATBOT / ENTERPRISE AI
Choose an AI deployment that fits your organization. Start with a managed cloud environment or deploy on infrastructure you control, with options designed around your security, operational, and business requirements.
Plan Your AI DeploymentYour AI platform can be planned around the way your organization operates.
YOUR AI PLATFORM. YOUR DEPLOYMENT CHOICE.
Deployment Flexibility
Choose the environment that fits your organization
YOUR DEPLOYMENT STRATEGY
One platform. Multiple paths.
Align your AI environment with your operational needs.
Choose where your AI platform operates.
Conceptual illustration, not a product screenshot.
THE OPPORTUNITY
Organizations have different requirements for technology, security, data handling, and day-to-day operations. Some prefer a managed cloud service, while others need to run their AI platform within infrastructure they control.
Deployment flexibility gives your organization options. You can discuss a hosted environment, a private cloud, an on-premises data center, or an air-gapped deployment for suitable use cases.
The right approach depends on your business goals, available technical resources, security policies, and how you plan to use AI agents across your organization.
Illustrative deployment planning workflow
Users and AI goals
Security and privacy
Systems and resources
Managed cloud
Private cloud or data center
Air-gapped options
Configure the platform
Connect approved systems
Support and improve
KEY BENEFITS
Select the deployment approach that supports your team today and gives you options as your needs develop.
Use a hosted cloud environment with platform infrastructure managed for your organization.
Deploy the platform in your own cloud or on-premises environment.
Plan the environment around your access, privacy, and governance requirements.
Discuss deployment and migration options as your organization’s requirements change.
DEPLOYMENT OPTIONS
Each organization has its own balance of operational resources, infrastructure preferences, and security requirements.
Use a managed cloud environment while the hosting infrastructure, monitoring, and routine platform operations are handled for you.
Focus: managed operations.
Deploy within your organization’s cloud account or private cloud environment using your selected infrastructure and policies.
Focus: control in your cloud.
Run the platform in your own data center or other on-premises infrastructure.
Focus: local infrastructure control.
RESTRICTED ENVIRONMENTS
Some organizations require additional control over network access, data movement, and the AI models used in their environment.
Discuss a disconnected environment using compatible locally hosted models and platform components.
Review government cloud options based on the agency’s infrastructure and security requirements.
Consider supported self-hosted AI models when external model services are not appropriate.
The feasibility of a restricted deployment depends on the selected models, infrastructure, integrations, installation, and update processes.
FIND THE RIGHT FIT
Both managed and self-managed approaches can support enterprise AI. The best choice depends on your requirements.
A hosted environment can help your team concentrate on configuring agents and serving users while infrastructure operations are managed.
Focus: managed platform operations.
A self-managed deployment can align the platform with your existing cloud, data center, and operational practices.
Focus: infrastructure control.
Discuss a future move from hosted to self-managed deployment when your organization is ready.
Focus: long-term flexibility.
DATA AND SECURITY
Your AI environment should fit the way your organization manages identity, access, data, and operational oversight.
Review where organizational information is stored and processed within the chosen deployment.
Plan authentication and role-based permissions around your organization’s policies.
Consider network boundaries, connections, and external services as part of the architecture.
Define how platform activity is monitored and reviewed by authorized teams.
Choose supported model options that align with your data and operating requirements.
Establish responsibilities for updates, oversight, and ongoing administration.
Data handling and compliance depend on your complete deployment, selected model providers, integrations, agreements, and operating practices.
AI MODEL FLEXIBILITY
Different use cases may call for different AI capabilities. Review model options alongside your infrastructure and data requirements.
Consider models from supported providers when external services align with your policies.
Consider compatible locally hosted models for environments requiring additional control.
Review model configurations as your use cases, available technology, and requirements evolve.
SOURCE CODE AND OWNERSHIP
Deployment planning can include a discussion of source code access, licensing, customization, and the responsibilities of your technology team.
Explore source code access to support technical review and understanding of the platform.
Discuss how your team can adapt the platform and integrations to your organization’s needs.
Review the applicable license terms, deployment services, updates, and ongoing support.
DESIGNED FOR DIFFERENT ORGANIZATIONS
Deployment requirements vary by organization and use case. Choose an approach that fits the people and information you support.
Support employees, customers, and operations through a deployment aligned with business needs.
Plan AI services for learners, faculty, and staff around institutional requirements.
Review infrastructure and security needs for workforce and public service applications.
Consider deployment and access controls around your firm’s confidentiality requirements.
Plan AI infrastructure around institutional governance, security, and data policies.
Review deployment and data handling needs for healthcare teams and their workflows.
GETTING STARTED
Begin with your goals and requirements, then select an environment that supports your intended AI use cases.
Identify your users, AI goals, security requirements, and available technical resources.
Compare managed hosting, your own cloud, on-premises infrastructure, and restricted options.
Configure the platform, launch an initial use case, and review results and operational needs.
FREQUENTLY ASKED QUESTIONS
Deployment flexibility means your organization can discuss different environments for its AI platform, including managed hosting and infrastructure it controls.
Managed hosted deployment provides the platform through a cloud environment where hosting infrastructure and routine platform operations are managed for you.
Yes. Deployment in a supported cloud account or private cloud environment can be planned around your organization’s infrastructure and security requirements.
Yes. On-premises deployment can be discussed based on your infrastructure, operational resources, and project scope.
An air-gapped deployment can be considered using compatible local models and components. Installation, updates, and connected-system requirements should be reviewed.
Yes. A move from hosted deployment to your own infrastructure can be discussed as your requirements change. Migration scope, licensing, and operational responsibilities should be reviewed for your project.
Supported commercial and self-hosted model options can be reviewed based on your use cases, infrastructure, and data policies.
Begin by discussing your AI goals, systems, security needs, and preferred operating approach. Your team can then evaluate the deployment options that fit your organization.
LET'S GET STARTED
Explore how a flexible deployment can help your organization launch AI agents in an environment aligned with its needs.
Discuss Your AI DeploymentWe can discuss your infrastructure, data, security requirements, AI models, and deployment options.
