SOVEREIGN AI · PRIVATE DEPLOYMENT · OPERATIONAL CONTROL

Operational Sovereign AI. Inside your boundary.

Dropp Cortex deploys private models, governed agents, secure knowledge systems, and AI operations inside your cloud or data center — so your data stays yours and AI delivers measurable operational outcomes.

On-premisePrivate CloudAir-gappedOpen-sourceAuditable

Infrastructure alone isn't the outcome

Sovereign AI infrastructure is only useful when it becomes part of real business operations. Cortex connects private AI infrastructure to governed agents, workflows, knowledge, monitoring, and measurable outcomes.

Four layers of sovereignty

“Sovereign AI” means more than where a model runs. We design for control at every layer.

Data Sovereignty

Your documents, prompts, and outputs stay inside the boundary you define — and never train a shared or third-party model.

Model Sovereignty

Open-source or custom models you can inspect, fine-tune, version, and replace — with no dependency on a single external vendor.

Deployment Sovereignty

Runs in your cloud account, your data center, or an air-gapped environment — the infrastructure boundary is yours to define.

Operational Sovereignty

Your team can operate, audit, and extend the platform after handover — not locked into an ongoing black-box dependency.

Built for organizations that can't treat AI as a public API call

Sovereign AI is the right track when data control, auditability, or jurisdiction are non-negotiable.

EnterpriseGovernmentRegulated industriesTelecomFinanceHealthcareIndustrial organizationsData residency & security requirements

The problems this track exists to solve

Business and regulatory constraints we design around from day one.

  • Data residency or sovereignty law prevents sending regulated data to a public AI API.
  • Procurement and security review reject any architecture with unclear data flow to third parties.
  • Public model APIs create vendor lock-in and no visibility into how a model is versioned or changed.
  • Legal, compliance, or audit teams need to trace exactly what a model saw and why it produced an output.
  • Sensitive IP, financial data, or health records can't be exposed to an external inference endpoint.

Reference architecture

A layered platform, not a single model endpoint — each layer deployed inside your boundary.

01

Data & Knowledge Layer

Document ingestion, secure vector storage, and retrieval — indexed inside your environment.

02

Model Layer

Private LLM serving and fine-tuning, with version control over every deployed model.

03

Agent & Workflow Layer

Governed agents that take permissioned actions against your systems, with defined guardrails.

04

Observability & Governance Layer

Logging, evaluation, access control, and audit trails across every request.

Deployed as a set of services inside your infrastructure — not a call to an external black box.

Deployment models

The right boundary depends on your constraints, not a one-size-fits-all pitch. Each model has its own data flow — never a mix of “fully on-premise” and “calls an external API.”

Dropp-managed environment

  • Data flow: Runs in infrastructure Dropp operates on your behalf; data does not pass through third-party AI APIs
  • Ownership: You own the data and outputs; Dropp operates the environment under contract
  • Model hosting: Private model instances, isolated per client
  • External dependencies: None by default — no public model API calls
  • Operational responsibility: Dropp Technologies operates and maintains the platform
  • Suited for: Teams that want a private deployment without running their own infrastructure

Customer private cloud

  • Data flow: Deployed inside your cloud account (AWS, Azure, GCP, or private cloud); traffic never leaves your tenancy
  • Ownership: You own the infrastructure, data, and access controls
  • Model hosting: Private model instances running on your cloud compute
  • External dependencies: None required; optional outbound access can be restricted or disabled
  • Operational responsibility: Shared — Cortex deploys and tunes, your team owns infrastructure and access
  • Suited for: Organizations with an existing cloud footprint and internal security requirements

Customer on-premise infrastructure

  • Data flow: All inference, storage, and orchestration run on hardware inside your facility
  • Ownership: You own the hardware, data, and network
  • Model hosting: Models served on your own GPU/server infrastructure
  • External dependencies: None — no internet dependency required for inference
  • Operational responsibility: Your infrastructure team, with Cortex providing MLOps and support
  • Suited for: Regulated industries, government, and organizations with strict on-site requirements

Air-gapped deployment

  • Data flow: No network path to the public internet at any point; updates delivered via controlled physical or offline transfer
  • Ownership: You own and physically control the entire environment
  • Model hosting: Fully isolated models, vector stores, and agents inside the air gap
  • External dependencies: None whatsoever — zero external connectivity by design
  • Operational responsibility: Your security and infrastructure teams; Cortex trains your team for independent operation
  • Suited for: Classified, defense, or maximum-sensitivity environments

Hybrid deployment

  • Data flow: Sensitive data and models stay inside your boundary; only non-sensitive, explicitly approved tasks may reach an external API
  • Ownership: You define exactly what is allowed to leave the boundary, task by task
  • Model hosting: Private models for sensitive workloads, external APIs only where explicitly permitted
  • External dependencies: Limited and explicit — scoped per workflow, never a blanket connection
  • Operational responsibility: Shared between Cortex and your team, governed by an agreed data-flow policy
  • Suited for: Organizations that need sovereignty for most workloads but flexibility for a few

What runs inside the boundary

The same platform capabilities, wherever your boundary is drawn.

Private LLM & Model Serving

Deploy and fine-tune open-source or custom models, with full version control and no shared multi-tenant inference.

Secure & Air-Gapped RAG

Enterprise search and retrieval over your own documents, fully isolated from public networks when required.

Governed Agents

Agents that act on your systems within explicit permissions, approval steps, and audit logging — not open-ended autonomy.

MLOps & GPU Optimization

Inference scaling, memory management, and model monitoring, built on Dropp Technologies' infrastructure experience.

Identity, Access & Auditability

Role-based access, SSO integration, and full request-level audit trails across models and agents.

Observability & Evaluation

Ongoing evaluation of model outputs, drift detection, and logging built for compliance and quality review.

You own it after we hand it over

Every engagement includes documentation and hands-on knowledge transfer to your team — so the platform stays operable, auditable, and extensible without an ongoing dependency on Cortex.

From pilot to production

A staged path that proves value before you commit to full-scale deployment.

01

Scope

We define the boundary, the data flow, and the success criteria for a limited pilot.

Week 1–2
02

Pilot

A working deployment against a real use case, inside your chosen boundary.

Weeks 3–8
03

Production

Hardening, access controls, and monitoring for full operational use.

Weeks 9–14
04

Operate

Ongoing support, or full knowledge transfer to your internal team.

Ongoing
Common questions

Questions people usually ask about Sovereign AI

What does “sovereign” actually mean here?

It means data, model, deployment, and operational control all stay inside a boundary you define — not just where a server happens to sit.

Can this run fully air-gapped?

Yes. In an air-gapped deployment, there is no network path to the public internet at any point — updates are delivered through a controlled offline process.

Do we have to choose open-source models?

No. We most commonly deploy open-source models for full control and inspectability, but the architecture also supports licensed or custom models where that fits your requirements.

How long does a pilot take?

Most pilots are scoped for 6–8 weeks against a single, well-defined use case, so you can evaluate real results before committing to production.

What happens to our data during the engagement?

Data flow is defined explicitly for your chosen deployment model before work starts — see the deployment models above for exactly what does and doesn't leave your boundary.

What do we own at the end of the engagement?

You own the deployed models, data, and infrastructure. Documentation and knowledge transfer are part of every engagement so your team can operate independently.

Scope a pilot

Tell us about your data, your boundary, and your constraints.

No generic pitch — we'll scope a pilot around the deployment model that actually fits your requirements.

or reach us directly