AI development.

LLM applications

Software where a language model does a defined job: answering, drafting, classifying, or pulling structured fields out of text. The model is a component inside a system, not a substitute for the system.

AI agents

Programs that can take a sequence of steps, using tools you choose, inside limits set at the start. An agent still has a scope. It does not get to invent one.

RAG and knowledge assistants

Assistants that answer from material you provide, with a way to see where an answer came from. We don’t invent a corpus you don’t have, and we don’t present a guess as if it were a source.

AI automations and integrations

A repeated task, connected to tools you already use, with a person still able to stop it. Useful when the work is frequent and the rules are clear enough to write down.

MCP servers

Model Context Protocol servers that expose one system, dataset, or workflow to AI clients. The access is the access you intend, and nothing extra for the sake of a demo.

ML forecasting

Models for a measurement you actually have: demand, load, or another series in your own data. A forecast is a model of that history. It is not a guarantee of the next quarter, and it is not built from data that isn’t yours.

How the check works

AI does the drafting, generating and editing where it saves time. A person checks the work before you see it. AI makes mistakes, so the rule is simple: nothing about your business, product or property gets invented, and anything AI-altered gets labelled where it matters.

Most of this only holds if the surrounding software is sound: the API, the data, the permissions. That is why AI development sits next to custom software, not instead of it.