Managed private AI
Managed private AI, deployed and run for you
Managed private AI is a done for you deployment of a private GPT. We provision a dedicated cloud environment for your company, connect it to your approved documents and systems, secure it with role based access and audit trails, and then operate it for you. Your team gets a branded AI assistant that answers from your own data, and you get an infrastructure partner who handles hosting, updates, model management, and support. The timeline is scoped around your data, integrations, permissions, and review requirements.
Existing customer proof
“We needed an AI platform that understood our business, remembered our clients, and kept our data private.”
K3 Technology · managed IT services firm
Inspect the managed deployment story
The published K3 case study shows the customer context, approved-source connection, sourced-answer workflow, and managed setup currently available as attributed proof on this site.
What the managed service includes
Deployment in your cloud
We stand up a Microsoft Azure environment dedicated to the deployment, apply your branding, and put the assistant on the approved domain. The environment boundary is documented in the proposal and technical review.
Data connection and indexing
We connect and index approved sources like SharePoint, Google Drive, network file shares, CRMs, and accounting tools, so the assistant answers from your real documents with sources.
Security and access controls
Role based access, encryption at rest and in transit, and available audit data are configured to the approved scope. Model providers, endpoints, retention terms, and training terms are reviewed and documented for the deployment.
Model management
Supported model options can be made available through one interface. We configure approved defaults for the work in scope and review provider changes as part of the support plan.
Ongoing operation and support
We monitor the environment, apply updates, handle hosting, and support your users. You do not need to hire machine learning engineers or run infrastructure to keep it healthy.
Training and adoption
We onboard your team, build the first repeatable workflows, and make sure people actually use the assistant instead of reaching for public tools.
Managed service or a project you own
Both paths can end with private AI. Only one of them adds a standing engineering commitment to your business.
Doing it yourself
Self hosting an open source model means renting GPUs, standing up the serving stack, wiring authentication and access controls, connecting your data, patching for security, and keeping the whole thing running. That is a standing engineering commitment a company should choose deliberately.
Managed private AI
You get a documented deployment boundary without taking on the full build and maintenance workload. We configure and operate the service under the agreed responsibilities, while your team owns the business decisions and source approvals.
What ongoing management covers
- Environment monitoring and uptime management
- Security patching and dependency updates
- Model updates and new model rollouts as they ship
- New data source connections as your needs grow
- User onboarding, access changes, and support
- A named point of contact who knows your deployment
See the mechanics on the how it works page, review the security model, prepare a policy with the AI data handling workbook, or check pricing.
Managed private AI questions
What is a managed private AI deployment?
It is a private GPT that we deploy and operate on your behalf. Instead of buying software and running it yourself, you get a dedicated AI environment in your own cloud, connected to your data and secured to your rules, that we host, maintain, and support as an ongoing service.
What is included in your managed GPT service?
Deployment in a dedicated Azure environment, connection and indexing of approved documents and systems, configured access and security controls, supported model options through one interface, and the monitoring, updates, model management, user support, and training defined in the proposal.
How is managed private AI different from self hosting an open source model?
Self hosting means your team owns the servers, serving stack, security patching, access controls, and maintenance. A managed deployment assigns those operating responsibilities in the service scope so your team does not have to build and maintain the full stack itself.
Do we need our own IT or AI team to run it?
No. That is the point of a managed service. We handle provisioning, security, updates, and support. Your IT staff can be involved where it helps, for example approving data sources, but they do not have to build or maintain anything.
What does managed private AI cost?
Pricing depends on your team, approved data sources, access controls, integrations, deployment environment, and support needs. Review the pricing factors or book a meeting to talk through the scope.
How long until it is running?
The timeline depends on the systems being connected, the access model, integrations, and any legal or security review. We map those requirements first, then propose a deployment plan.
Let us run your private AI
Book a meeting to see a live managed deployment and talk through your data sources, controls, integrations, timeline, and pricing.