Services
AI agents and LLM integration
Dahlinius builds language models into your systems: AI agents that do work, MCP servers that give models controlled access to your data, and integrations with Azure OpenAI, Anthropic or other providers. In your own code.
What we build
Models from Anthropic, OpenAI via Azure and open models, chosen by requirements on data handling, cost and quality.
- AI agents that call your tools and APIs, with confirmation where it has consequences
- MCP servers that expose your systems to any LLM in a controlled way
- LLM integration in existing .NET, Java or Python systems
- Search over internal documents with the right permissions
- Evaluation, logging and cost control
How it works
We start with a concrete use case, not a strategy. A short review of data and permissions, then a working prototype in your code within a few weeks.
What works gets hardened: error handling, evaluation, logging and deployment to Azure.
Experience
Skogsradar has a RAG-grounded assistant that answers questions about the user's own property, citing its sources in every answer. AgentPurge runs a custom MCP server with over 16 tools and agent loops across Anthropic, OpenAI, Gemini, Groq and Ollama. Both are live today.
All of this is built in our own products, where we made the calls and ran the result ourselves. In your environment the equivalent is Azure AI Foundry, Azure OpenAI or Semantic Kernel, with stricter limits.
Data handling
Your data can stay in your environment. Azure OpenAI in a European region, clear limits on what the model may do, logging of every call. What may leave the environment is your decision, and it is the first question we ask.
Frequently asked questions
Which LLM providers do you work with?
Anthropic, OpenAI via Azure OpenAI and open models. The choice is driven by data handling, cost and quality.
What is an MCP server?
An open standard, Model Context Protocol, that lets a language model call tools and read data in a controlled way. Build the integration once, switch models freely.
Can you build AI into existing systems?
Yes, and that is how we prefer to work. In .NET, Java or Python, in the code you already have, rather than something on the side.
How do you keep an agent from doing the wrong thing?
Limit its tools, require confirmation for actions with consequences, log everything and evaluate against real cases before it acts on its own.
Do you have a use case?
Describe it in a few lines and we will assess what is reasonable to build.