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Most AI offerings put your data in someone else’s multi-tenant cloud, under someone else’s terms. Infradapt runs AI workloads on private, single-tenant infrastructure it owns and operates. Your documents, prompts, and outputs stay in your custody — they never become another company’s training data.
Most organizations discover shadow AI only after sensitive data has already left through unapproved public tools. Infradapt starts with usage policy, role-based access, audit logging, and compliance-aligned controls — so employees get AI productivity inside guardrails leadership can actually see and defend.
Infradapt earns no commissions for pushing one AI platform over another. Model and vendor selection is independent: the right model for the job, sized to your actual workloads. Because our fee is fixed, our incentive is to prevent problems and control costs — not to grow your consumption.
AI depends on identity, security, network, and data infrastructure. Infradapt manages the whole stack, so AI is engineered into your environment rather than bolted on top of it — one team, one ticket, one accountable relationship for AI and everything it touches.
For teams adopting AI in daily work — training, guardrails, and practical enablement so employees use AI productively and safely.
For organizations that need prevention, monitoring, and incident response around the same data their AI now touches.
For workloads that belong on private, single-tenant, share-nothing infrastructure — the same foundation sovereign AI runs on.
For businesses that want one accountable team running the IT foundation — helpdesk, infrastructure, and vendor management — under the same fixed-fee model.
Businesses need AI. The question is whether it runs on your terms or someone else’s. Managed Private AI gives your team the productivity without surrendering your data, your budget, or your compliance posture to an outside platform.
Start with an AI readiness review. Infradapt will assess your workflows, data sensitivity, compliance posture, and automation opportunities, then define the right sovereign AI model for your organization.
Managed Private AI is artificial intelligence deployed for your business workflows in a dedicated private environment: single-tenant, vendor-independent, with governance built in from access controls to audit logging to shadow-AI discovery. Infradapt delivers and manages the whole environment for one fixed monthly fee.
No. Your documents, prompts, and outputs stay inside your private environment on Infradapt-controlled infrastructure. Nothing is shared with public AI platforms or used to train anyone else’s models. That custody is the core of the sovereign model: your data works for you alone.
Enterprise AI subscriptions can be configured well, and the platform still decides the architecture, the roadmap, the pricing, and where your usage data lives. Managed Private AI moves those decisions to your side: dedicated infrastructure, model choice, your policies enforced and logged, one fixed monthly fee.
Shadow AI is employees using unapproved public AI tools with company data: customer records, contracts, financials, outside any control or audit trail. It is now one of the most common sources of data leakage. A sanctioned, capable alternative with guardrails takes away the reason to wander.
No. Regulation raises the stakes, but any business with client data, contracts, pricing, or financials has information it should keep close. If your data has value to your business, it has value to others. And when regulation does apply, compliance lives in the whole operating practice: identity, access, encryption, logging, personnel, and procedure, aligned to each workload. That is the part we run.
One fixed monthly fee, scoped to your environment and use cases, consistent with how Infradapt delivers every managed service. The assessment defines scope and fee before anything is deployed, and scope changes only by agreement. Budgets stay flat in between.
The AI readiness review comes first: workflows, data sensitivity, compliance posture, and use-case selection. Initial deployments typically follow in weeks, not months, starting with one or two high-value workflows and expanding as governance and results prove out.
