Moving your data to the cloud is the easy part. Doing it inside an organization that cannot afford to be wrong is the job.

Most migrations stall in the approval queue, not the pipeline

If you work in banking, insurance or healthcare, you already know the pattern. The architecture gets signed off in weeks. Then the security review, the vendor questionnaire, the change-advisory board and the test-evidence requirements take months — and the project sits still while the meter runs.

Most vendors treat that period as dead time. We treat it as build time.

While your review runs, we build and validate the entire migration in an isolated lab on synthetic data and technical metadata. When clearance comes through, we are not starting. We are deploying something already proven.

On our most recent engagement, that single decision is why the platform landed on the timeline it did.

What we do

Cloud and hybrid data migration

Moving data and the applications that depend on it, with a rollback path at every step and no big-bang cutover.

Near real-time architecture

We design several candidate architectures against your actual latency requirement and pick one on evidence rather than preference. On our most recent build, commit-to-queryable time measured 2 to 4 minutes under controlled load and 2 to 8 minutes in production — and we can tell you which single setting moves that number.

Application modernization

Refactoring legacy applications so they use the cloud rather than merely sit on it.

Hybrid optimization

Most regulated organizations will run on-premises and cloud side by side for years. We design for that reality instead of pretending it is a transition state.

Post-migration performance

Scalability and cost are the same problem viewed from different angles. We size for both.

How we work

Our Strategize · Energize · Datagize method, applied to migration:

• Strategize — Readiness assessment and migration roadmap. What moves, what gets rebuilt, what stays, and in what order.
• Energize — System and data architecture, and a working proof of concept, validated in our environment before it touches yours.
• Datagize — Execution and post-migration optimization, sequenced so the business keeps operating throughout.

The stack

We are vendor-agnostic on advice and specific in practice. Recent work runs on:

• Snowflake — layered architecture: raw CDC landing, staging, dimensional consumption, row-level security, and an automated governance layer
• Azure SQL source systems, replicated by enterprise change-data-capture tooling
• Micro-batch pipelines that process deltas rather than reprocessing history
• Automated reconciliation that checks the target against source on every load
• Version-controlled schema — every database object in a repository, generated deploy scripts, and automated drift detection

That last one matters more than it sounds. When your auditor asks what changed and when, the system can answer.

Proof

We recently designed and deployed a multi-entity general ledger data hub on Snowflake for a Tier-1 financial institution — in production, under full change control, with no customer records or PII available to us at any point during the build.

Read the case study →