PLATFORM
We analyze the systems you already run, architect the data and AI platform on the pattern enterprises operate in production, bring it live in your environment, and hand it to the people who run the business.
ENGAGEMENT
An engagement has four responsibilities: understand the estate, architect the platform the operators will run, bring that platform live in their environment, and leave it with the teams who use it every day.
We start from the systems the organization already runs. The team records the systems of record, which data may move, the identity provider, and the decisions the platform has to support. Constraints are written before a design is proposed: who may see which data, which actions need approval, what must be recoverable, and what the operating team can support after handover.
Sources stay the systems of record. Connectors, validation, and access control sit in front of storage and retrieval. An application passes the request to an agent workflow, which calls a model-serving endpoint and, through MCP, only the tools policy allows. Kubernetes schedules those containers. A GPU is requested only by the serving workload. Logs, metrics, and approval sit on the same path.
We deploy into the environment the organization will operate, on its identity, its network, and its change process. Operators are present while a request is authenticated, governed data is retrieved, the model is called, and a tool returns only what policy allows. Health checks, metrics, and recovery are in place before the path is opened to the business.
Handover is the close of the engagement. The people who run the platform day to day can deploy a change, read the telemetry, recover a workload, and explain which data an agent was allowed to use. Runbooks, access, and ownership sit with those teams. We stay through the first production period long enough to confirm the operating rhythm. After that, the system is theirs.
ARCHITECTURE
This is the platform the engagement leaves behind. Each part has an owner. The arrows are the paths we prove with your operators before handover.
Sources stay the systems of record. Connectors, validation, and access control run before storage and retrieval. The application passes a request to an agent workflow. That workflow calls a model-serving endpoint and, through an authenticated MCP server, only the tools policy allows. Kubernetes schedules the containers. A GPU is attached when that serving workload needs it.
Interactive diagram. Activating a component or connection explains it and highlights the related path. The rest of the diagram stays visible.
Identity, policy, authorization, audit, and telemetry apply across the platform. They are not a final step after the others.
Databases, documents, APIs, and event streams stay systems of record.
Connectors ingest sources. Validation and access control run before storage and retrieval.
Validated responses return to enterprise applications. Approval applies to sensitive actions, not to every read.
The application passes the request to an agent workflow. The workflow may retrieve governed data, call a model, invoke MCP tools, evaluate the result, and repeat.
MCP is the protocol boundary for approved tools. It is not the model, the GPU, or the orchestrator. An MCP server may run inside or outside Kubernetes.
Kubernetes schedules the serving containers. NVIDIA GPUs run compatible inference for that workload. Agents, applications, and MCP servers are not GPU workloads by default.
Diagram guide
Select a component or connection. Its role is explained here, and the rest of the diagram stays in view.
IN OPERATION
Each path is a production concern we design with your operators, test in your environment, and leave in a state they can run.
We wire the request the business will run: source systems into the governed platform, the agent workflow operators own, model serving sized for the workload, and the application that receives the result.
Read the computing briefingThe workflow calls the model and, through MCP, an authorized server. The result comes back for evaluation.
We put the agent workflow in front of a model and, through MCP, only an authenticated server for an authorized tool. Results return to the workflow. The team evaluates them before the business relies on the action. MCP stays the tool boundary.
Read the computing briefingThe GPU is used only by this serving workload.
We schedule the model-serving container on Kubernetes and attach an NVIDIA GPU only when that workload needs it. Agents and MCP servers stay off the GPU.
Read about GPU schedulingWe land source data through ingestion, validation, and transformation, and we put access control on the platform before analytics or a model can use it.
Read about data platformsWe deploy the release, prove the health checks, and leave monitoring in the hands of the operating team. Capacity scales from those signals. Recovery replaces a failed instance under the same controls.
Read about the control loopWe bind access to identity and permission. Protected data opens only after that check, and the access is written to an audit event the business can review. Network location is not the decision.
Read about zero trustNext step
Tell us about the system, the constraint, and the outcome you need. We will respond with a practical path forward.
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