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 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. There is no internal AI project to staff, and most companies go from first call to a live private deployment in about two weeks.
What the managed service includes
Deployment in your cloud
We stand up a dedicated Microsoft Azure environment for your company, apply your branding, and put the assistant on your own domain. Nothing is pooled with other customers.
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, audit trails, and a SOC2 friendly architecture. Model calls run under enterprise agreements with zero data retention, so your data never trains a public model.
Model management
Access to 30 plus models including Claude, GPT, Gemini, and open source, all through one interface. We set sensible defaults for each type of work and keep the model lineup current for you.
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 most companies of 10 to 200 people do not want to own.
Managed private AI
You get the same private outcome, your data in your environment, without the build and the maintenance. We deploy it, secure it, and run it as a service. Your team gets the assistant, and your leadership gets one accountable partner instead of a new internal project.
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, 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 your documents and systems, role based access and security controls, access to 30 plus models through one interface, and ongoing operation covering monitoring, updates, model management, and user support. Team training is part of every deployment.
How is managed private AI different from self hosting an open source model?
Self hosting means your team owns the servers, the serving stack, security patching, access controls, and maintenance. Managed private AI gives you the same private result without that engineering burden. We run the infrastructure so your team can just use the assistant.
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 is a monthly platform fee that covers your dedicated environment, hosting, and management, starting around $500 per month, plus a per seat rate in the $20 to $25 range. See the pricing page for the full plans, or book a demo for a fixed quote.
How long until it is running?
Most companies are live in one to two weeks. We provision the environment, connect your data, apply your branding, and train your team, then hand you a running assistant that we continue to operate.
Let us run your private AI
Book a 30 minute demo. We will show you a live managed deployment, map it to your data sources, and give you a fixed quote on the call.