Six ways we help organisations move to the cloud, ship AI/ML models and keep shipping. Take one phase or the whole journey.
Move to AWS, Azure or GCP with a landing zone and architecture built for where you are going, not a lift-and-shift of where you are.
Take a model from notebook to production with versioning, monitoring and a rollback path — and keep it working after launch day.
Web platforms and internal tools built on a modern stack by a small senior team, shipped in increments you can review.
Move legacy systems onto cloud-native, AI-ready foundations incrementally — no big-bang rewrite, no feature freeze.
CI/CD, infrastructure as code and observability — so deploying software or a model to production is routine, not an event.
Find out where AI actually earns its cost in your business before you commit engineering time to building it.
Four stages, each with an output you can review before the next begins. Nothing starts until you have signed off what came before.
We audit your current cloud footprint, data and ML maturity, and engineering workflow — and come back with a prioritised list of what is actually worth fixing first.
A target architecture and tech-stack decision record you can defend to your own engineers: cloud topology, model-serving pattern, data flow, and the trade-offs behind each choice.
Infrastructure as code, CI/CD for software and MLOps pipelines for models, built in your cloud account from day one — so shipping is routine, not an event.
Monitoring, cost controls and retraining pipelines that keep working after we leave, plus a documented handover so your team can run and extend it independently.
A 30-minute call, no charge and no sales script. Describe the problem and we will tell you honestly whether we are the right people for it.
