// BY THE NUMBERS
Programme metrics under management
From customer accounts we operate today.
SME-CLD-LIVE
< 2 wk
First workload live in landing zone
SME-CLD-ROLL
< 1%
Post-cut rollback rate
SME-CLD-COST
20-35%
Typical FinOps cost reduction
AWS, Azure, and GCP. Landing zones, migrations, and FinOps. Canadian-region-first when residency matters.
Landing zones, migrations, and FinOps. Multi-cloud where it matters, Canadian-region-first when residency does.
AWS, Azure, GCP with policy and cost guardrails baked in. SCPs, OUs, tagging strategy on day one.
Migrations with minimal refactor. Inventory, dependency map, wave plan. Cutovers that finish on time.
Tagging, rightsizing, commitments. Monthly anomaly reviews so a forgotten cluster doesn't end the quarter.
What a multi-cloud programme looks like once the first workload is live, the tags hold, and the bill stops growing.
// BY THE NUMBERS
From customer accounts we operate today.
SME-CLD-LIVE
< 2 wk
First workload live in landing zone
SME-CLD-ROLL
< 1%
Post-cut rollback rate
SME-CLD-COST
20-35%
Typical FinOps cost reduction
// IN PRODUCTION
FinOps belongs in the landing zone, not bolted on a quarter later. We bake tagging, rightsizing, and anomaly detection in from day one, so the bill stops growing the month we arrive.
The long-form context behind the work. Written by the engineer who runs the engagement, not a marketing team.
Hyperscaler-neutral from the first call, so we design for your workload, then pick the cloud that serves it. The landing zone comes first because a foundation built badly taxes every workload that lands after it. Each step below has its own checkpoint, from the policy-as-code guardrails to the post-cutover rollback rate. FinOps is baked in rather than bolted on, so the bill stops growing the month we arrive, not the quarter after.
Account topology, identity boundaries, and network connectivity on AWS, Azure, or GCP, with policy-as-code, tagging, and cost guardrails baked in on day one.
A dependency map of every workload, grouped into migration waves. Residency, cost, and workload fit decide which cloud each wave lands on, not vendor bias.
Minimal-refactor migrations cut over on schedule, validated before the old environment comes down. Rollback planned per wave, not hoped for.
Monthly rightsizing, deliberate commitment purchases, and anomaly reviews that catch the forgotten cluster before it ends the quarter.
The questions we hear before a first cloud call. Every answer is written by the architect who runs the migration.
These disciplines share the same senior team and tend to land together. Follow the thread to the next one.
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