Anyone can build it once. The question is whether anyone can safely change it afterward.

Most custom builds become somebody else’s problem

The pattern is familiar. A consultancy delivers something that works, hands over a folder of scripts, and leaves. Two years later nobody knows which version is running, what changed last March, or whether it is safe to touch.

We build the way a product team builds, because the handover is the point:

• Every database object under version control — one file per object, in a repository, with real history
• Generated deploy scripts rather than hand-maintained ones, so what ships is what was reviewed
• Automated drift detection that compares what is actually running against what is supposed to be

When your auditor — or your next engineer — asks what changed and when, the system answers. Nobody has to remember.

What we do

Custom applications

Bespoke tools for the problems your off-the-shelf stack will not solve, built only when buying genuinely will not do.

Data pipelines and integration

Micro-batch pipelines that process only what changed rather than reprocessing history. On our most recent build, commit-to-queryable measured 2 to 4 minutes under controlled load and 2 to 8 minutes in production.

API integration

Connecting systems that were never designed to talk, without creating a new thing that only one person understands.

Data architecture optimization

Reworking what exists so it carries the load you are about to put on it.

Test tooling

On a recent engagement we built a reversible load generator that pushed five to ten times peak production traffic through a general ledger and reverted every change afterward — the environment finished byte-for-byte identical to how it started. Yes, you can load-test a general ledger safely.

How we work

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

• Strategize — Alignment on what the thing is actually for, and whether it should be built at all.
• Energize — Requirements analysis, system and data architecture, and a prototype.
• Datagize — Development, deployment and ongoing support, delivered through whatever change process governs your organization.

Built for change control

If you work somewhere with a change-advisory board, you already know that delivery speed is governed by the approval process rather than by typing speed. Our changes ship with generated implementation plans and test-evidence workbooks — the approval gates are part of the pipeline, not friction applied to it afterward.

The stack

• Snowflake — procedures, tasks, streams and a layered schema
• Azure SQL source systems, replicated by enterprise change-data-capture tooling
• Python tooling for deploy-script generation, schema drift detection and load-test harnesses
• Streamlit-in-Snowflake for operational dashboards that run where the data already lives
• Git as the source of truth — one file per database object, generated deploy scripts, and real per-object history

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 →