CuxtonAI builds AI architecture that begins with where data is allowed to be not where it is most convenient to process it. Deployment model, model choice and integration design are determined by your organisation's data sensitivity, regulatory context and technical constraints.
Enterprise AI cannot rely on public black-box endpoints. Explore the 4 deployment postures engineered around your data sensitivity, network boundaries, and statutory regulatory requirements.
Architectural Flow Schematic
Verified network isolation, data custody boundaries, and external egress barriers.
AI infrastructure runs entirely inside your physical datacenter on client-owned appliances. Prompts, retrieval documents, and weights never cross your facility's network boundary.
National Defense, Central Banks, Intelligence, Sovereign Wealth & Critical Infrastructure
Requires internal hardware procurement, physical space, power, cooling, and on-site hardware maintenance management.
How the options compare on perimeter defence, hardware velocity, and statutory approvals.
Every layer from data connectivity to autonomous agent execution is engineered with the organisation's governance requirements in mind. AI does not bypass security boundaries, compromise data sovereignty, or override institutional permissions.
Integrates directly with your existing enterprise infrastructure, single sign-on (SSO), directory services, and network boundaries. AI systems respect established access hierarchies rather than overriding them, ensuring no unauthorized privilege elevation.
Connects operational databases, document repositories, and institutional knowledge lakes through secure, permission-aware retrieval pipelines. Data remains encrypted within approved boundaries, and retrieval strictly enforces user-level access rights.
Executes approved open-weight models, customized architectures, or private API endpoints within isolated compute enclaves. Cuxton configures compute location based on your latency, regulatory residency, and data sensitivity requirements.
Task-bounded agents, analytical assistants, and automation pipelines presented through approved internal interfaces, existing tools, or bespoke front-ends. High-consequence actions incorporate mandatory human approval gates.
AI queries dynamically inherit the requesting user's security context. If a user cannot access a file directly, the AI model cannot access it either.
Strict logical barriers prevent model outputs or prompt injections from escalating system permissions beyond pre-approved scopes.
Every prompt, vector retrieval path, model decision, and workflow action is recorded with full auditability for continuous compliance.
Every workload has constraints before it has a model: where the data is allowed to sit, how quickly the answer has to come back, how exactly the output has to be structured, and what it can cost to run. Those four things decide the architecture.
Four workloads we see often. Each one is decided by its constraint, not by preference.
Weights you hold, running on hardware you control.
Chosen when
Frontier capability, used only where the contract permits it.
Chosen when
A smaller model taught your vocabulary and output format.
Chosen when
Small models take the routine work; larger ones take the exceptions.
Chosen when
| Criterion | 01Open-weight, self-hosted | 02Commercial API, under contract | 03Fine-tuned for the domain | 04Routed ensemble |
|---|---|---|---|---|
| Where data is processed | Your hardware | Vendor, under contract | Your hardware | Mixed, by rule |
| Latency | Low, predictable | Network-bound | Lowest | Varies by route |
| Format precision | General purpose | General purpose | Highest | High, with review |
| Cost shape | Capital up front | Per call | Capital, then cheap to run | Tracks difficulty |
| Main trade-off | You run the infrastructure | Vendor dependency | Needs retraining | More to maintain |
Most deployments end up using more than one of these. Talk through your constraints and we'll tell you which ones apply including where you don't need us.
AI governance is not an afterthought. It is enforced at the network, model, and application boundary before any system processes institutional information.
Book a technical discovery conversation. We'll discuss your infrastructure constraints, data sensitivity requirements and the architectural options appropriate to your context.
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