Say no to AI that does not pay for itself
If a use case will not justify the infrastructure and operating cost of running it, we say so on the first call. A bad fit costs you more than a lost engagement costs us.
About LogicWise
We are a cloud & AI transformation partner built around a simple conviction: most AI and cloud initiatives fail as deployment problems, not modelling problems.
Who we are
Small teams, senior engineers, production-first thinking
LogicWise exists because too many cloud migrations and AI projects follow the same pattern: a promising proof of concept, a working notebook, an architecture diagram — and then nothing that actually runs reliably in production. Almost always the root cause is the same — deployment, monitoring and ownership were treated as an afterthought instead of part of the design.
So we start with an assessment, not a build. We look at your current cloud footprint, your data and model maturity, and your engineering workflow, and come back with a target architecture and a prioritised plan before any infrastructure changes or a single model ships.
From there we work in small, senior teams. There are no layers of junior engineers billed at senior rates — the people you meet at kick-off are the people building your pipelines. Software, infrastructure and models are held to the same standard: tested, monitored, and reviewed before they touch production traffic.
And we finish properly. Your infrastructure runs in your own cloud account, your models live in your own model registry, and documentation is written as we go rather than scrambled together at handover. If you decide to take everything in-house the day after launch, you can.
40+
Cloud & ML workloads deployed
15+
Models shipped to production
99.9%
Median deployment uptime SLA
2wk
Average time to first deploy
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.
Our principles
How we make decisions
These are the rules we hold ourselves to, including when they cost us work.
Estimate in ranges, not wishes
Single-number estimates are almost always optimistic. We quote ranges with the assumptions written down, and we tell you which assumptions are load-bearing.
Build the boring parts well
Monitoring, rollback paths, access control, cost alerts. Unglamorous work that decides whether your platform survives its second year in production.
Leave your team stronger
We pair with your engineers, document as we go and run handover sessions. Our success is measured by how well things run once we are gone.
Security is not a phase
Dependency scanning, secrets management and sensible data handling are part of the pipeline from the first commit, not a pre-launch audit scramble.
Ship early, ship often
A model or feature that has never seen real traffic is a hypothesis. We get a usable version live as soon as it is genuinely useful, then improve it with evidence.
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.
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.