Private AI your business controls
We design, deploy, and maintain private AI infrastructure for organizations where privacy, client contracts, or security requirements make full dependence on public AI platforms a poor fit.
Built for
- Law firms
- Medical organizations
- Financial firms
- Defense and government contractors
- Private security companies
- Companies with sensitive intellectual property
- Organizations whose contracts restrict data processing by third parties
Private where it matters. Frontier models where they make sense.
We do not think companies should choose between private AI and cloud AI on principle. The strongest architecture is usually hybrid.
Sensitive internal work runs on infrastructure you control. Demanding tasks that are not sensitive can still be routed to leading cloud models when they perform better.
Work that often stays private
- Internal document analysis
- Sensitive client information
- Company knowledge systems
- Proprietary research
- Confidential operational data
- Security workflows
Diagram of a hybrid AI setup. Inside your infrastructure sit company knowledge, private models, and sensitive workflows. A routing policy on the boundary decides where each request goes: sensitive work stays inside, and demanding tasks that are not sensitive can go out to cloud frontier models.
Why companies build private AI
Keep sensitive work under your control
Some information should not be processed outside infrastructure you control. Selected AI workloads can run in a dedicated environment built around your requirements.
Reduce dependence on one provider
AI providers can change pricing, rate limits, models, and policies. Owning part of your AI infrastructure gives you an alternative.
Dedicated capacity
Instead of competing for shared cloud compute, your team has capacity reserved for internal use.
Model flexibility
Private infrastructure can run multiple models and evolve as better ones are released.
Specialized workflows
Some lawful industries have needs that general AI products do not fit. A private deployment gives you more control over model selection and internal policies, within applicable law and licensing.
Private AI is not for everyone
For many businesses, cloud AI is easier and cheaper. We explore private AI when one or more of these is true:
- Your team uses AI heavily
- You handle sensitive information
- You need control over your infrastructure
- AI has become operationally critical
- You need dedicated capacity or model flexibility
- The economics of owned infrastructure start to make sense
Our job is to find out whether it makes sense for you, not to sell you hardware.
We handle the infrastructure
Most businesses do not want to become GPU infrastructure companies. You do not have to.
Setup
- Requirements and usage planning
- Hardware selection
- Deployment
- Model selection
- Company knowledge integration
- Security controls
- Employee access and onboarding
Ongoing management
- Model upgrades
- Monitoring
- Security updates
- Performance tuning
- New internal tools and integrations
- Employee support
- Capacity planning
You own the capability without having to babysit it.
The Private AI Assessment
In 45 minutes, we answer:
- What information actually needs to stay private?
- Which workloads should stay internal, and which should still use cloud AI?
- What compute would you need?
- What would the infrastructure and ongoing management cost?
- Where would the system live?
- Which models make sense?
- Would private AI actually improve your risk, economics, or operations?
If private AI does not make sense for your organization, we will tell you.
Common questions about private AI
Will private AI be cheaper than cloud AI?
Not automatically. For light usage, cloud AI is usually more economical. The case for private infrastructure grows with usage, privacy requirements, and how much the business depends on AI. We will tell you which side of that line you are on.
Can we still use ChatGPT, Claude, or other cloud tools?
Yes. Most designs are hybrid. Sensitive work stays private and everything else can use the best cloud model for the job.
Where does the hardware live?
In your office, in a colocation data center, or in a private cloud account you control. The assessment recommends one.
Who maintains it?
We can. Ongoing management covers model upgrades, monitoring, security updates, performance tuning, and support.
Does this make us HIPAA, SOC 2, or CMMC compliant?
Tell us your requirements in the assessment. We design around them and work alongside your compliance and legal teams, and we are clear about what the system does and does not cover.
Which models can run privately?
Many capable open weight models can be deployed privately. We choose based on your workloads and each model’s license terms.
Will you sign an NDA first?
Yes, on request. Tick the NDA box on the assessment form and we will send a mutual NDA before the call.
Find out if private AI makes sense for your organization
The Private AI Assessment is free and takes 45 minutes. You leave knowing what should stay private, what it would cost, and whether it is worth doing.