Private AI Systems, Agents & MCP Integrations

Private AI infrastructure

Useful AI on infrastructure you control

Kituwa IT helps businesses choose, install, and connect private AI systems without starting with an oversized server or a vague “AI transformation.” We begin with one real workflow, the data it needs, and the privacy boundary it must respect.

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Local or private-cloudArchitecture matched to the workload

Model comparison firstQuality, speed, licensing, and hardware

Scoped integrationsAgents, MCP tools, data sources

Documented handoffConfiguration, access, and next steps

What private AI means

Control the data path, not just the server

Fully local deployment

Prompts, documents, embeddings, and model requests can stay inside your environment when every required component is local and external telemetry is disabled.

Approved hybrid deployment

A local system can still use selected cloud APIs or business tools when the tradeoff is useful and clearly documented. Only the data required for those calls should leave the environment.

Privacy depends on the final architecture. External APIs, hosted connectors, telemetry, cloud backups, and remote support paths may transmit data outside the local server. We document those boundaries before launch.

Good starting points

Focused use cases with a measurable outcome

Private document assistant

Ask questions across policies, manuals, project files, or internal knowledge with source citations and access controls.

Workflow agent

Draft, classify, route, or summarize work with defined tools and a human approval step where it matters.

Internal AI gateway

Give approved users one controlled interface while routing different tasks to the models that fit them.

Model evaluation

Compare a small set of practical models against examples from the real workload before purchasing hardware.

MCP integration

Connect an existing MCP tool or create a narrowly scoped tool for a documented API and agreed operations.

Business data connection

Connect one database, API, file repository, or business system with only the permissions the workflow needs.

How the work proceeds

Evaluate, build, test, and hand off

Step 1

Define the job

We document the users, inputs, expected outputs, privacy requirements, and examples of good and unacceptable results.

Step 2

Choose the architecture

We compare models, hardware, local and hosted components, licensing, latency, and ongoing operating cost.

Step 3

Build and connect

We install the agreed stack, configure access, add only the approved tools and sources, and document the data path.

Step 4

Test with real examples

We test quality, citations, failure cases, permissions, and performance before handing over the system.

Fixed-scope packages

Start with the piece you actually need

Planning

Use-case & model evaluation

$499

Discovery, comparison of up to three practical approaches, hardware guidance, privacy review, and a written recommendation.

Choose evaluation

Deployment

Private AI remote setup

$1,499

One supported runtime on one compatible host, one basic interface, access configuration, testing, and documentation.

Choose setup

Agent

Custom agent creation

$1,999

One defined agent and workflow using supported existing tools. New connectors are separate.

Choose agent

MCP

Connect an existing tool

$699

Configure, authorize, test, and document one compatible existing MCP connector.

Choose connection

MCP

Create a custom tool

$1,499

One narrowly scoped tool for a documented API with up to three agreed operations.

Choose custom MCP

Data

Connect one external source

$899

One supported database, API, file repository, or business system with agreed permissions.

Choose data connection

Knowledge

Private document search

$1,499

One approved document source, retrieval setup, citations, access rules, initial indexing, and testing.

Choose document search

Scope boundaries

What is included—and what needs a quote

Included in the selected package

The fixed deliverables shown above
Remote configuration on compatible systems
Basic testing and handoff notes
One agreed revision pass within the package scope

Quoted separately

Hardware and operating-system repair
Paid APIs and commercial licenses
Complex identity, high availability, or multi-site design
Travel, data cleanup, ongoing monitoring, backups, and managed support

Frequently asked questions

Before you purchase

Should I start with the evaluation?

Yes if you have not already chosen compatible hardware, a model, and a narrow first workflow. The evaluation is designed to prevent an expensive but poorly matched deployment.

Does self-hosted mean no data ever leaves?

Only if the full design is local and all external services are disabled. Hybrid systems can intentionally use approved external APIs or backups. The architecture should document each boundary.

Are hardware and model licenses included?

No. We can recommend compatible hardware and identify relevant licensing, but purchase costs are separate.

Can one package cover several departments?

Usually not. Fixed packages cover one defined workflow, host, connector, or data source. Broader deployments receive a custom scope.

Start with one useful system

Confirm scope before buying a build package

Use the website inbox to describe the users, data, hardware, and first workflow. If the design is still unclear, begin with the $499 evaluation.

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Plan for life after launch

Keep users confident with practical AI training, arrange workflow care, and scope host maintenance separately. Workflows and server upkeep have different responsibilities and labor allowances.