Building on our phased Strategize. Energize. Datagize. methodology, we offer a comprehensive range of complementary services to address the diverse data challenges organizations face today, creating a seamless path from strategy to implementation.
Our five core service areas align with this methodology to ensure results at every step of your data journey:
optimizes your infrastructure for the future
risk-managed migration patterns for banking and finance
delivers tailored solutions to unique challenges
near-real-time data hubs
Together, these service areas provide the depth and flexibility needed to meet any data-related challenge, whether you require targeted expertise or a comprehensive transformation effort
We are vendor-agnostic on advice and specific in practice. Recent work runs on Snowflake and Azure SQL, with enterprise change-data-capture replication, micro-batch pipelines that process deltas rather than reprocessing history, row-level security, automated reconciliation on every load, and version-controlled schema with automated drift detection.
When your auditor asks what changed and when, the system can answer.
The judgment calls behind the work, and where we’ve written them up.
We match the old numbers before we improve them. A migration’s first job is parity with the legacy system, even where the legacy logic is arguably wrong. Quietly “fixing” numbers nobody asked to change starts a new argument with finance. How we ran Phase 2
We test against what runs, not what’s written down. Documentation drifts. On one engagement, logic built strictly to the documentation produced a count more than 70% too high. Testing against the production system caught it before cutover. The full story
We measure before we promise. “Near real-time” is a claim until it’s a number. On our reference build: 2–4 minutes under controlled load, 2–8 minutes in production, with the one setting that moves it identified. The measurements
Reconciliation is a control, not a project phase. Independent checks compare the hub with its sources on every load, for as long as it runs. The case study
Fortune 500 data programs across 10+ industries. You work with the founder, not a placement.
The entire migration was built and validated on synthetic data, inside the bank’s own change-control process.
