Runs in your account
The agent, models, and logs live in your own AWS account – in Switzerland if you choose. Your data never leaves it to reach a model. No shared tenant, no copy on our side.
Solutions – Agentic AI
AI that does real work – with the guardrails production demands.
Most AI pilots stall between the demo and the day shift. We build AI that reaches production – supervised, monitored, and accountable for what it does. It runs in your own cloud account, on your data, and we operate it for as long as you want.
From pilot to production
The demo is the easy part. This is what makes AI safe to run:
Built on AWS (Bedrock for agents, SageMaker for models) – model-agnostic, so you're never locked to one AI vendor.
- a readiness check first – is your data good enough to carry the use case?
- your data stays in Europe
- fixed price, from pilot to production in weeks
How it works
An agent is only as safe as the fence around it.
The model is the small part. Most of the work is the harness that decides what the agent may touch, watches what it does, and stops it when it's wrong.
Scoped, not trusted
An agent gets narrow, least-privilege permissions to the exact systems its use case needs – read-only where reading is enough. It cannot reach anything outside the scope we grant.
Grounded in your data
Answers come from your machines, ERP, and documents through retrieval – not from the model's training. Every answer traces back to the source, so an operator can check it.
Human in the loop
Reading and drafting run on their own. Any action that changes something – raising a work order, writing to a system of record – pauses for a person to approve.
Logged and reviewable
Every prompt, tool call, and action is recorded – what the agent saw, decided, and did. When something looks wrong, you can replay it and see why.
Cost capped
Token and inference spend is metered per use case with hard ceilings. A runaway loop hits its limit and stops before it becomes a surprise on the bill.
Whose account, whose keys
Built with your engineers. Never locked to us.
We build it with your engineers, in your account, so the knowledge stays when we leave. We operate it for as long as you want – and hand over the keys whenever you ask. During the engagement, our access is a role you grant in your own account – scoped, time-boxed, and logged; you can revoke it at any time. You are never required to run what we build, and never trapped into it either.
Proof
Proof
Little Legends – generative AI, in production on AWS
A generative-AI product running in production on AWS – SageMaker for image models, Bedrock for text, a serverless core that scales with demand, reviewed against AWS's own quality framework. It's a consumer storytelling app, not a factory – proof that we ship AI to production and keep it running, not a predictive-maintenance reference.
Predictive maintenance on machine data is a capability we build on your data, not a deployed reference we can name yet.
Read the Case Study
Bring us one AI use case.
Bring us one AI use case.
A workshop finds the use case worth proving – in your environment, on your data – and tells you honestly whether your data can carry it.