Controlled boundaries
You decide which knowledge, tools and work sit inside the private environment.
Private AI Core
We can design and deliver a governed foundation that connects your people, assistants, models and business systems inside a boundary you define. Start with one use case, or design the whole Core with our senior team.
The progression
Five tiers to a unified AI operating system.
Progression by need, not by pitch.
AI maturity is not measured by how many tools an organization owns. It is measured by how well people, workflows, data, automations and AI systems work together. Our commercial focus is Tiers 2 through 4. Tier 0 is not a service we sell, and Tier 1 is selective.
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Tier 0 · Not offered
Research
Exploring AI, comparing tools and identifying possible use cases before picking a production problem.
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Tier 1 · Selective
Single workflow
One defined process improved with one focused automation and a narrow operational objective.
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Tier 2 · Core offering
Multiple workflows
Several automations built and coordinated across a team, function or related process portfolio.
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Tier 3 · Strategic
System unification
AI tools, assistants, automations, applications and data operating through shared architecture.
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Tier 4 · Flagship
Private AI Core
The approved environment unified around a private, governed core for models, knowledge, assistants, tools and controls.
Open any tier to see what it sounds like, what tells you it is time to move, and what we do.
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“One process is manual, slow or error-prone.” We run Prioritize and Scope on that one workflow, then build it in the approved environment against a measured baseline, designed so it can join a portfolio later. Tier 1 is selective: we take it on when it creates meaningful standalone value or an intentional foundation for a broader architecture. What tells you it is time to move: a second and third workflow are identified and they share data or approvals.
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“We have several use cases and they should be managed as one program.” We prioritize workflows by business value, readiness and risk, design shared data, integration, approval and exception patterns, then build and connect the automations across approved applications and data sources. Every workflow is tested, documented, monitored and measured the same way. Knowledge, controls and ownership can still sit with each individual workflow at this tier. What tells you it is time to move: shared tools, data, identity or controls are now required across more than one workflow.
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“Our AI tools, agents and automations are in silos and duplicate access and governance.” We map the current environment, design a unified target architecture, and build MCP endpoints, APIs, adapters and reusable connectors to approved tools and data. Common identity, permissions, orchestration, policy, logging and monitoring move from per-workflow to shared. What tells you it is time to move: you want private model routing, your own knowledge behind the assistants, or a boundary you control.
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“We want control over our data, models, infrastructure and roadmap, and our own people able to build on it.” We design the deployment boundary, then stand up private model and knowledge services, orchestration, governed integration and trust controls, and decide with you who operates it afterward. This is less a step up than a change of operating mode. The Core is a destination, not a prerequisite. Nobody has to start at Tier 4 and nobody is pushed there.
MCP, in plain English. Model Context Protocol is a standardized doorway through which approved AI assistants can use approved business tools and information. It is one integration option; the security comes from the identity, authorization, network, logging, data-handling and approval controls we design around it.
Why organizations get here
Architecture answers what a policy document cannot.
Public AI tools are useful until the questions get specific: which data may the model see, where does inference run, who approved the answer and what did it cost. We answer those questions with architecture designed against your requirements.
Right model, right task
Routine and sensitive work runs on private models. Approved frontier models stay available for advanced reasoning.
Shared enterprise context
Every assistant and workflow draws on the same approved knowledge.
Measurable operations
Quality, performance, adoption and model usage are instrumented to stay visible after launch.
What we build
Five layers on a boundary we design with you.
A Core is not a single model or a chatbot. It is the foundation we can design so every AI-enabled workflow in the organization shares the same approved knowledge, controls and infrastructure, instead of each one being built as a separate island.
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Experience and assistants
The interfaces your people will use: assistants, Command Center modules, workflow entry points, approval gates.
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Orchestration and routing
Routing that sends each request to the right model class under policy, with human sign-off where judgment matters.
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Private model and knowledge
Private inference, retrieval over your approved documents and data, controlled memory, and evaluation sets.
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Governed integration
MCP endpoints, APIs and connectors to the systems you already run.
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Trust and operations
Identity, least privilege, encryption, audit trails, monitoring, backup, rollback and cost controls.
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Deployment boundary
Your hardware, an isolated private cloud, controlled model APIs, or a hybrid. Defined with you in Design.
Privacy, security, performance, availability and compliance characteristics depend on the approved architecture, technology choices, client environment and operating controls.
Where the Core can run
The boundary is a design decision, not a product.
The private boundary is designed with you. Requirements may call for infrastructure you control, an isolated cloud environment, controlled model APIs, or a hybrid. The deployment pattern is fixed during solution design, once your obligations and volumes are documented.
Your own hardware
A server or workstation you control. The clearest boundary and the lowest ongoing cost. You own patching, capacity and physical security.
An isolated private cloud
Dedicated instances in a tenancy you control, with private networking and customer-managed keys. Faster to stand up and easier to scale. The boundary is contractual and configuration-based, so it has to be evidenced rather than assumed.
Controlled model APIs
Approved external model services under enterprise terms, with zero-retention agreements where available. Used where the workload does not justify private infrastructure.
A hybrid
Routine and sensitive work stays private; harder reasoning routes to approved frontier models, with policy deciding what may cross and logging every crossing. The pattern most clients land on.
Private does not mean isolated, and private deployment on its own does not make an environment secure or compliant. We do not recommend hardware or a model before your requirements are on paper.
Build your own LLM
It rarely means training a model from scratch.
Prospects who say they want to build their own model usually mean one of four different things, and they cost wildly different amounts. Sorting this out in the first conversation stops the engagement being scoped around the wrong problem. We will tell you when the cheaper option does the job.
Deploy an existing model privately
An approved open-weight model runs inside your boundary. No training. For organizations that want control over where inference happens. We handle model evaluation and selection, serving, routing and monitoring.
Connect a model to private knowledge
Retrieval gives the model your approved documents and data at query time. Nothing is trained into the model. This is the majority of real demand: knowledge search, assistants, document workflows. We handle ingestion, indexing, access-aware retrieval and source attribution.
Adapt a model
Fine-tuning on a well-defined task with a suitable dataset. Worth it only when evaluation shows prompting plus retrieval is not enough — consistent format, domain vocabulary, classification at scale. We run the feasibility check first and measure against the un-adapted baseline.
Develop a specialized model
Substantial model engineering, potentially pre-training. Rare, and it requires a justified use case, large proprietary data, real compute and an operating plan. We scope this as a separate feasibility engagement before anyone commits.
We do not train foundation models from scratch, and we do not sell a Private AI Core to a single-workflow problem.
Next step
Find the right starting point.
Whether you need one workflow automated or a private AI environment your team will run, we will map the architecture, the risks, the operating model and the next practical step. The first conversation is about your boundary and your use cases, not about hardware or model names.