A model that works in a notebook isn't a production system yet. Our MLOps services build the pipelines, monitoring, and deployment infrastructure that keep AI systems performant, observable, and cost-efficient long after launch day.
Serving UK and EU clients: GDPR, EU AI Act, data residency, and VPC / on-premise deployment options are covered on our Trust & Safety page.
MLOps is the operational layer that keeps a machine learning or AI system running correctly after it ships — the pipelines that retrain and redeploy models, the monitoring that catches performance drift before customers notice, and the CI/CD that lets your team ship changes without breaking production.
Most of the AI systems that fail in production don't fail because the model was wrong. They fail because nobody was watching when the data shifted, the costs crept up, or a deploy silently broke something.
Every MLOps engagement covers the same three layers, whether we're building from scratch or retrofitting an existing model.
Automated retraining, testing, and deployment pipelines so model updates ship the same way your other code does — reviewed, tested, and reversible.
Production-grade serving infrastructure with continuous monitoring for latency, error rates, and model drift — so degradation is caught before it's a customer complaint.
Infrastructure sized and monitored for actual usage, not worst-case guesses — built on a privacy-first architecture. See our Trust & Safety framework.
The questions we get asked most about MLOps and AI infrastructure — answered straight, no sales pitch.
MLOps is the infrastructure and process layer that keeps an AI system reliable after it ships — pipelines that retrain and redeploy models, monitoring that catches performance drift, and CI/CD so changes don't break production. You need it the moment an AI system is handling real traffic and a silent failure would cost you something. A one-off prototype doesn't need it; a production system does.
It depends on how many models are in production, how much existing infrastructure we're building on top of, and how mature your monitoring already is. We price by milestone, not by the hour, so the number is fixed before work starts. Book a call and we'll scope it in detail.
Yes — most of our MLOps engagements start with a model or prototype that already exists but lacks the pipelines, monitoring, or deployment infrastructure to run reliably in production. We build around what's already there rather than starting over.
Performance and cost metrics tracked continuously — latency, error rates, model drift, and token or inference spend — with alerting before a silent regression becomes a customer-facing problem. See our LLM integration work for how this applies specifically to cost control on LLM-based systems.
Book a 30-minute call — we'll tell you honestly what it would take to make your AI system reliable in production.