Cloud Strategy & Migration
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.
Learn more about Cloud Strategy & Migration
Cloud & AI transformation partner
Architect, deploy and scale your cloud & AI
LogicWise helps organisations move to the cloud, ship AI/ML models to production, and build on the latest tech stack. We assess, architect and deploy — then hand it over running in your own cloud account.
Cloud-native architecture
Production-grade MLOps
You own the infrastructure
What we do
Cloud & AI, covered end to end
Engage us for a single phase or the full journey — from the first cloud or AI assessment through to a platform or model running in production with your team supporting it.
AI & Model Deployment (MLOps)
Take a model from notebook to production with versioning, monitoring and a rollback path — and keep it working after launch day.
Learn more about AI & Model Deployment (MLOps)Custom Software Development
Web platforms and internal tools built on a modern stack by a small senior team, shipped in increments you can review.
Learn more about Custom Software DevelopmentApplication & Platform Modernisation
Move legacy systems onto cloud-native, AI-ready foundations incrementally — no big-bang rewrite, no feature freeze.
Learn more about Application & Platform ModernisationDevOps & Platform Engineering
CI/CD, infrastructure as code and observability — so deploying software or a model to production is routine, not an event.
Learn more about DevOps & Platform EngineeringAI Strategy & Advisory
Find out where AI actually earns its cost in your business before you commit engineering time to building it.
Learn more about AI Strategy & Advisory
How we work
Assess. Architect. Deploy. Scale.
Four stages, each with a defined output you can review before the next one starts. No stage begins until you have signed off the one before it.
Assess
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.
Architect
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.
Deploy
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.
Scale
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.
40+
Cloud & ML workloads deployed
15+
Models shipped to production
99.9%
Median deployment uptime SLA
2wk
Average time to first deploy
Why LogicWise
Cloud & AI delivery that behaves like your own team
We work the way a good in-house platform team works — close to the infrastructure, honest about trade-offs, and accountable for what happens after launch.
Cloud-native by default
We design for the cloud you are moving to, not the servers you are moving from. No lift-and-shift you will have to re-architect in a year.
Production-grade MLOps
Models ship with versioning, monitoring, drift detection and a rollback path — not as a notebook someone eventually has to productionise.
You own your infrastructure
Your cloud account, your model registry, your repositories, from day one. Nothing runs on infrastructure you cannot see or access.
Secure & compliant pipelines
Dependency scanning, secrets management and access control are part of the pipeline from the first commit, not a pre-launch checklist.
Technology
We pick the tools to fit the problem
No house framework we push on every client. These are the platforms we know deeply enough to support in production.
Python
PyTorch
TensorFlow
LangChain
OpenAI & Anthropic APIs
React
Next.js
TypeScript
Node.js
AWS
Azure
GCP
Kubernetes
Docker
Terraform
PostgreSQL
pgvector / Pinecone
Client feedback
What working with us is like
“They rebuilt our deployment pipeline in three weeks. We went from a risky Friday-night release to shipping mid-week without anyone noticing.”
B2B SaaS platform
“Our recommendation model had been stuck in a notebook for a year. LogicWise had it serving live traffic with monitoring and a rollback path inside a month.”
E-commerce, UK
“What stood out was that everything ran in our own cloud account from day one. No black box, no dependency on them to keep it running.”
Fintech scale-up
Questions
The things clients ask first
Do you only do AI projects, or general cloud/software work too?
Both, and usually together. Most engagements combine cloud infrastructure, deployment pipelines and at least one model or AI feature — we specialise in the point where software delivery and AI/ML delivery meet, not purely in one or the other.
How do you price a project?
An initial assessment is a fixed fee scoped to the size of the problem. After that you get a costed plan with a range rather than a single optimistic number, and a written change process if scope moves. Longer engagements are usually priced per sprint so you can stop at any milestone.
Do we own the infrastructure, code and models?
Yes, entirely and from day one. We build in your cloud account, your repositories and your model registry wherever possible, and all intellectual property transfers to you under the contract. There is no lock-in to us as a supplier.
Can you work alongside our in-house team?
Frequently. We can supply a complete delivery team, embed cloud/ML specialists into your existing squads, or own a defined workstream — such as the MLOps pipeline — while your team handles the rest.
What happens after a model or platform goes live?
Most clients move onto a support agreement covering monitoring, incident response and an agreed allocation of time each month for retraining or improvements. If you would rather run it yourselves, we do a structured handover with documentation and pairing sessions.
Tell us what you are trying to deploy
A 30-minute call, no charge and no sales script. Describe the cloud or AI problem, and we will tell you honestly whether we are the right people for it.