Cloud-ready isn't enough — build AI-native
We transform your infrastructure from cloud-capable to AI-native — rearchitecting data, compute and tooling so production AI stops being a promise and starts compounding as advantage.
we start with your stack, not a slide deck
/ the pilot trap
Most AI projects
never ship.
85 in 100AI pilots stall before reaching production. The models aren’t the problem — the infrastructure underneath them is. Data silos, manual compute, no ML platform, no deployment pipeline.
stall before production
Gartner 2024
faster with right infra
Databricks 2024
15 of 100 AI pilots reach production · Gartner 2024
/ what we build
Five layers.
All connected.
AI-native infrastructure isn’t one thing — it’s five layers working together. Most organisations have some of them. We modernise what’s missing and wire up what you already have.
Click any layer to see what we modernise there.
GPU/CPU on-demand, auto-scaling clusters — compute configured for ML workloads, not general-purpose web traffic.
- GPU / CPU on-demand
- Auto-scaling clusters
- Spot + reserved optimisation
- Multi-cloud ready
/ why it matters
From 48 weeks
to 18 weeks
Without an AI-native foundation, every project re-fights the same infrastructure battles. With one, time to first AI workload in production compresses by 62% — and the gains compound with every new use case.
Unstructured DIY path
48weeksWith AMDIM
18weekssame scale · hover bars for detail
30w savedKey milestone
DIY
AMDIM
Data accessible to models
18w
4w
First model trained
32w
12w
First model in production
48w
18w
/ the cost of building on unstable ground
What infra debt
costs your team
AI/ML engineers spend nearly half their time on infrastructure plumbing — not model work. Set your team size and see what an AI-native foundation returns.
Engineering capacity lost to infra plumbing / year
You keep / year
$396.0KEngineers stuck on plumbing
2.5 FTEs redirected to modelsAI cycles the team can run / year
5.0 more cyclesAn AI-native foundation returns 2.5 FTEs to model work — ≈ $396.0K of engineering capacity a year.
/ how we work
AI-native in 18 weeks
Five phases, layer by layer — so existing systems keep running while your stack becomes AI-native underneath them.
18-week timeline
Total · 18 weeks to AI-native
Assess
We audit your existing stack layer by layer — compute, data, ML platform, security and cost. You leave with a precise gap map and a ranked remediation backlog, not a vague slide deck.
- Compute & GPU access audit
- Data architecture & silo mapping
- ML platform and tooling inventory
- Security posture and compliance baseline
You'll have
AI Infrastructure Readiness Report — gap map, risk rating, prioritised backlog
/ the proof
Numbers that
hold up
Every recommendation starts with a number. Every engagement ends with one.
AI pilots stall before reaching production
Gartner 2024
faster model-to-production with purpose-built ML infrastructure
Databricks 2024
cite infrastructure gaps as their top AI scaling barrier
IBM Institute for Business Value 2023
All figures sourced and verified — not invented, not inflated.
/ the depth
Beyond the basics —
the full toolbox
5 capabilities, each backed by a real toolbox — rehost / replatform to rearchitect / AI-native. A taste below; the full library runs deep.
techniques · 10 disciplines
Assess & plan
know the estate before you move it
Migrate app & code
from monolith to modern, safely
Migrate data & analytics
move the data, keep the numbers
Land on cloud
a governed platform to run on
Assure
parity proven, cutover reversible
/ your industry
The AI your industry wants —
and what’s blocking it
For each thing you want AI to do, there’s an infrastructure gap in the way — across your whole stack, not just data. Pick your industry for a taste; the full picture, grouped by layer, lives on the industries page.
Data foundation
Fraud AI that scores in-flight, not overnight
Data foundation
See the whole customer, not one product at a time
Compute & edge
GenAI copilots on the bank's own data, safely
Compute & edge
Stop AI compute costs running away
/ why it pays
Outcomes you can measure
faster model deployment
AI-native data foundation
Unified lakehouse architecture and real-time serving — the substrate every model, agent and copilot runs on.
less infrastructure waste
Purpose-built ML platform
MLflow, Ray, Kubeflow or cloud-native — experiment tracking, model registry and CI/CD wired into your stack.
audit-ready from launch
Security & governance built in
Data lineage, access controls and audit trails from day one — not bolted on after the first audit finding.
Gartner 2024 · Databricks 2024 · IBM Institute for Business Value 2023 — verified, not invented.
/ what we deliver
End-to-end, not half-built.
/ before you commit
Fair questions
No — cloud modernisation lifts workloads to the cloud. AI Modernisation rearchitects specifically for ML workloads, data serving and model lifecycle management. It starts where CloudOps ends. See also MLOps & AIOps for the operational layer once your stack is in place.
See where you stand in 5 minutes.
Take the AI Infrastructure Readiness Scorecard — a tailored score and the fastest path to impact.