AI integration

When to get in touch

• You want to use company documents and knowledge in AI-assisted work.
• You need to connect AI to an internal application or its API.
• You are considering a custom MCP server or connecting to an existing one.
• You have a pilot and want to review data flows, permissions, and operations.

Choosing the right connection

We start by defining what AI should do and who will use its output. Finding information in documentation requires a different design from changing records in a business system. We separate reading data, proposing a change, and executing it.

Depending on the task, we can use application APIs, document retrieval, or MCP. An MCP server gives an AI client an agreed interface to tools and data. It does not by itself guarantee correct answers or cover all access controls; the integration design must address those.

Data, permissions, and evaluation

We identify the necessary data, its sources, and who may access it. For external services, we discuss which information is sent and the terms under which it can be used. For operations that change data, we define confirmation, logging, and error handling.

Representative tasks help us evaluate output quality, missing-information handling, and unavailable systems. AI can produce incorrect answers; we design human review and measurement appropriate to the task’s impact.

Data security: cloud or local models?

Model choice affects where your data goes, who can access it, and what it costs to operate. I help compare cloud APIs, models running in your own infrastructure, and a combination of both based on data sensitivity, output quality, and expected usage.

When cloud makes sense

A cloud model can suit a quick pilot, variable workloads, or tasks requiring a particular model’s capabilities without managing your own compute environment. Before connecting company data, we review the specific service’s terms: use of inputs for training, retention, request logs, processing location, and access controls. Business API terms may differ from those of a consumer chat application.

We send only the information needed for the task. Where appropriate, we remove identifiers or replace sensitive details before sending them. This alone does not guarantee anonymity; we also assess context from which information could be inferred.

When to consider a local model

A local model can suit sensitive documents that need to stay within your infrastructure, reduced dependence on an external service, or selected functions that must work without internet access. We validate whether its quality, speed, and hardware requirements fit the task.

With a properly designed deployment, data can be processed without sending it to an external model provider. Running a model locally is only part of the design: we also review embeddings, connected tools, telemetry, logs, and backups. Permissions, updates, and security cover the whole solution, including APIs and MCP servers.

Where you can save

With regular, sufficiently high usage, a local model can reduce costs compared with paying for every cloud API request. We evaluate savings against your workload, including hardware, electricity, administration, maintenance, availability, and output quality. Cloud can be less expensive for small or occasional workloads.

A hybrid approach is also possible: process sensitive tasks locally and send selected other requests to a cloud model. Routing rules must prevent sensitive data from being automatically sent to the cloud during an error or fallback.

What you can receive

• An integration design connecting applications, data sources, and an AI client.
• A working pilot for an agreed task.
• An MCP server connection or design and implementation of the required server tools.
• Documentation of data flows, permissions, and operational dependencies.
• A comparison of cloud and local operation, costs, and sensitive-data protection.
• Evaluation criteria and a list of next steps.

We agree on specific deliverables and implementation scope before work begins.

How we start

Describe one repeated activity and the available systems and data. We select a focused pilot where benefits and errors can be observed. After validation, we decide whether and how to extend it.

Common questions

Do we have to use MCP?

No. The choice depends on the AI client, existing interfaces, and the integration goal. A direct API or document retrieval may be sufficient.

Can AI write to our systems automatically?

Where interfaces and agreed permissions support it, those operations can be designed. We first define their impact, review steps, and error handling. Wider workflows can involve process automation.

Is local AI automatically safer and cheaper?

Not automatically. Local operation gives you greater control over data and can save money with a suitable workload. Security depends on the entire solution, while costs depend on usage, hardware, and administration. We compare both options against a specific task.