What “AI-ready” means
Between a proof of concept running on a data scientist's laptop and an AI service in production sits an entire layer of engineering: the right compute at the right cost, data arriving clean and traceable, models versioned and observed, predictable latency, controlled access, data staying where the law says it must stay.
That layer is infrastructure, not data science. And it is exactly what our Cloud and DevOps engineers do for a living.
What we build
- Compute: GPU clusters on cloud or on-premise, scheduling, job queues, utilisation without waste.
- Data pipelines: ingestion, transformation, feature stores and vector databases for RAG and semantic search.
- Serving and gateways: models exposed as reliable services, with caching, rate limiting, fallbacks and versioning.
- End-to-end MLOps: model registry, CI/CD for machine learning, canary rollouts, drift monitoring.
- Observability and costs: GPU, token and latency metrics in dashboards the CFO understands.
- Security and compliance: workload isolation, access management, data in the EU, GDPR by design.
The point
We don't sell models and we don't do research: we build and run the platform that puts them to work. Your data science team thinks about the product, our engineers keep everything else standing. On European cloud or in your data centre.
Let's talk about your next engineer.
Tell us about your collaboration opportunities. We reply with concrete profiles and a reduced-rate pilot-project proposal.