Your data, your rules.

Data sovereignty is a simple idea: you own and control your data, and no third party can access it, train on it, or route it somewhere you never agreed to. This page explains what that means in practice, and why we build everything around it.

The risk nobody signed up for

Cloud AI created a category of risk most organizations never formally evaluated. The terms of many SaaS AI tools permit training on what your people type in. Queries can route through data centers in foreign jurisdictions. Vendor price changes arrive with no leverage on your side. And when an automated decision goes wrong, “the AI did it” is not an answer your auditor, your regulator, or your client will accept.

None of this is an argument against AI. It is an argument about where AI should live.

Five questions to ask any AI vendor

Including us. A vendor with good answers will not mind you asking.

Where does our data live?

On whose hardware, in which country, under whose law? With Modular the answer is one sentence: on our servers, in a FedRAMP-certified US data center, under US law.

Who can access it?

Can the vendor read your prompts? Train on them? Our models run on infrastructure we operate. No model provider sees your inputs, and nothing is used for training.

What happens when pricing changes?

Consumption-based billing means the bill is a surprise and the leverage is theirs. We charge fixed monthly pricing, so the number you budgeted is the number you pay.

What is the exit plan?

If you leave, what do you take with you? Everything we build for you is built to leave: your data, your workflows, your policies, and your audit history.

Can you reconstruct why the AI acted?

Logging that an agent sent an email is not the same as logging why it decided to. Every agent action on our infrastructure is reconstructable from the audit trail.

Is the approval gate structural?

A policy written in a prompt is a suggestion the model can ignore. Our approval gates and policy rules are enforced in configuration, at the infrastructure layer, where a model cannot bypass them.

What private AI means here

Privacy as empowerment, and never as an add-on. It is the architecture underneath every service we sell.

  • Models run locally on infrastructure we operate, from the physical facility to your login screen
  • No third-party cloud exposure for private deployments, and reviewed data boundaries when you choose a commercial platform
  • Model-agnostic by design: use the model that fits the task, never the one a vendor locks you into
  • Structural approval gates, audit logs, and human review queues on every agent deployment
  • We use what we sell: our own operations run on the same stack every day

Where it shows up: Private AI Workspaces, AgentWorks, AI Governance and the fractional CAIO, and compliance-first managed services. Watching the power bill too? See the energy-efficient private AI option.

Kick the tires

The fastest way to evaluate private AI is to use it. Our public chat demo runs on the same private infrastructure we sell, or bring the five questions above to a free consultation and ask them of us first.

Or schedule a free consultation.