Architecture, migration, and the work that keeps systems upright.
The engagements people call us for when the system works fine in staging and stops being fine at month end — and the ones where everything has to move to a new platform without the business noticing.
Current-state assessment, target-state design, and a multi-year roadmap that survives contact with next year's budget.
Assessment and disposition of every workload, landing zone design, migration waves, and a cutover plan that includes how to roll back — across AWS, Azure, and GCP.
Schema mapping, transformation, and reconciliation, with row-level proof that what arrived matches what left. The unglamorous half of every migration and the half that goes wrong.
Strangler-fig decomposition of monoliths and mainframe-adjacent systems, sequenced so the business keeps running while it happens.
Warehouses, lakehouses, and the pipelines feeding them — built so the numbers reconcile and somebody owns each dataset.
Load-tested and failure-tested systems, designed to degrade gracefully instead of falling over at the worst possible moment.
Load testing, profiling, and database tuning to close the gap between your SLA targets and what you're actually serving.
Nobody remembers a migration that went well.
That's the goal. Migrations fail in predictable places — an inventory that missed something, a dependency nobody documented, a cutover with no way back, and data that arrived subtly wrong and wasn't checked. So that's what the method is built around.
Every workload and dataset found and given a decision: rehost, replatform, refactor, retire. The retire column is usually larger than anyone expects.
Networking, identity, guardrails, and cost controls built before the first workload lands, not retrofitted around it afterwards.
Migration sequenced by dependency and blast radius, and rehearsed against production-like data before the real cutover.
A cutover with a tested rollback, then row-level reconciliation proving the data that arrived matches the data that left — before anything is decommissioned.
Measured before, measured after.
An architecture opinion nobody can test is just an opinion. Every engagement starts by establishing what "good" is in numbers, so the improvement is arguable at the end rather than asserted.
Baseline first
We measure the current system before proposing changes to it — including the parts everyone assumes are fine.
Change one thing at a time
Bundled changes make it impossible to know which one worked. We sequence deliberately.
Leave the harness behind
The load tests and dashboards we build are yours afterwards, so the next regression is caught without us.