AI engineering
AI is now part of how we build and of what we build. We wire large language models into your products and workflows, give them access to your data and tools in a controlled way, and use AI coding agents every day to deliver faster without lowering the bar on quality.
The tools
Agentic loops, LLM APIs, RAG, MCP and AI-assisted coding, built into software that ships.
LLM APIs
Claude, GPT, Gemini and open-weight models such as Llama and Mistral, called through their APIs. We choose the model per task based on quality, speed, cost and where your data may be processed.
Agentic loops
Agents that plan, call tools, check the result and repeat until the job is done: triaging tickets, preparing quotes, updating records. Each loop has a step limit, logs every action and asks a person before anything risky.
RAG
Retrieval-augmented generation grounds answers in your own documents, policies and product data. Content is chunked, embedded and indexed in a vector store such as pgvector, and every answer cites its sources.
MCP
The Model Context Protocol is the open standard for connecting AI assistants and agents to your systems. We build MCP servers for your CRM, databases, files and internal APIs, each with scoped, read-only-by-default permissions.
Vibe coding
AI-assisted development with Claude Code, Cursor and GitHub Copilot. We go from idea to working prototype in days, then every generated change is reviewed, typed, tested and checked into version control before it ships.
Evals & guardrails
Test sets built from real cases, run on every prompt or model change, plus input and output checks for personal data, prompt injection and off-topic answers. Quality is measured, not guessed.
How we work with them
Human in the loop
Agents propose and people approve whenever an action spends money, sends a message to a customer or changes important data. Automation grows as trust in the results is earned.
Your data stays yours
Enterprise API terms with no training on your data, EU or regional hosting when required, personal data masked before it reaches a model, and secrets kept out of prompts and logs.
Model-agnostic design
A thin provider layer means switching to a newer or cheaper model is a configuration change, not a rewrite. Prompt caching and routing simple tasks to small models keep the bill predictable.
Measured in production
Every AI feature logs accuracy, latency and cost per task, with dashboards your team can read, so you see whether it pays for itself.
Planning a project on this stack?
Tell us what you are building and we will tell you how we would approach it.