The parts that make AI reliable.
The demo is the easy part. Reliability is the work. We wire your sources through retrieval, keep the model swappable, and gate every answer through guardrails and evaluations before it reaches a user.
Retrieval-augmented generation
Answers grounded in your own knowledge, with citations. The model works from your data, not from memory.
Agents that do work
Multi-step agents that take actions against your systems, scoped and observable, not a black box.
MCP integrations
A secure, standard way to connect models to your internal systems and external APIs. Your data stays where it belongs.
Evaluations & guardrails
A reference set, measured quality, and guardrails that keep the model inside what it is allowed to say.
Measured, grounded, in production.
01Grounded by default
RAG over your sources with citations, so answers trace back to something real.
02Evaluation-driven
We measure quality against a reference set and track it over time. AI quality you can see, not a vibe.
03Observable and guarded
Logging, guardrails, and human-in-the-loop where it matters. You know what the AI did and why.
04EU-hosted, model-flexible
EU-hosted by default, and the model stays swappable. We choose per use case, not by lock-in.
Senior engineers, not prompt jockeys.
Founder-led delivery by senior engineers with over 10 years building and running production systems. We treat AI as software: tested, observable, and maintainable, with the same standards as the rest of your stack. EU-hosted and GDPR by default.
Founder-led delivery
The people you scope the AI with are the people who build and ship it.
10+ years in production
Real systems run under load, not slideware. AI treated as software, tested and observable.
Engineered, not prompted
Maintainable code, evaluations, and guardrails held to the same standard as the rest of your stack.
EU-hosted, GDPR by default
German engineering standards, your data in Europe, auditable by design.
From a feature to a full assistant.
A governed internal assistant
RAG, MCP, role-based access, and audit-safe logging, packaged into an internal AI knowledge assistant, live in 8 weeks.
AI Knowledge AssistantAI features, hardened
Built an AI feature with AI tools and need it production-ready? We harden it across the six pillars.
Production-ReadyCommon questions.
How do you stop the AI from making things up?
We ground answers in your own data with RAG, cite sources, and put guardrails and evaluations around the model so it stays inside what it is allowed to say. We measure this, we do not hope for it.
Which models do you use?
We pick the model per use case and keep it swappable. We are not locked to one provider, and we choose for quality, cost, and data-residency fit.
Where does our data go?
EU-hosted by default, and we connect to your systems through MCP so your data stays where it belongs. Nothing leaves Europe unless you decide it should.
How do you know it actually works?
Evaluations. We build a reference set, measure quality against it, and track it over time, so AI quality is something you can see, not a vibe.
Let's put AI into production.
Tell us what you want the AI to do. We will tell you straight what it takes to make it reliable.