Fragile pipelines, undocumented transformations, and a warehouse sized for 2019. We modernise data platforms incrementally - legacy and modern running in parallel - so the business never stops while the infrastructure catches up.

The pipeline has been running for four years. Nobody is sure who built the original version or why certain transformations are done in that order. Changing anything in the middle requires understanding everything that comes after it. The data warehouse was sized for the business as it was five years ago. Every quarter, the queries run a little slower. The finance team has started building their own exports because the official process takes too long.

Fragile pipelines accumulate workarounds. Data silos form not because teams want them, but because the cost of integration across systems keeps rising. The result is an analytics capability that the business cannot trust at the pace it needs.

Modernisation is not a technology preference. It is a response to specific constraints: what the current platform prevents the business from doing, and at what cost. We start with that question, not with a stack recommendation.

How We Engage

Assessment → Phased Migration → Handover. No big-bang rewrites.

The Discovery Workshop (3–10 days) is the typical entry point for organisations that need the assessment before committing to a migration. For organisations that already know what needs to move, we scope the migration directly.

What We Deliver

Target Architecture Selection

We recommend based on actual use cases, data volumes, team capabilities, and existing investments - not vendor preference. Each choice documented so your team can evaluate it independently.

We work with your existing stack or recommend based on the situation. The migration discipline matters more than the tooling.

Data Contract Design

Explicit agreements about schema, delivery cadence, and acceptable change notification between producers and consumers - established before migration begins. When something changes upstream, it is detectable and accountable rather than discovered by accident in a broken dashboard downstream.

Phased Migration Without Disruption

Legacy and modern systems run in parallel throughout. Each component is validated under production load before traffic shifts. No scenario where the business is without reliable data for weeks while a rewrite completes. Rollback is always available.

Team Handoff & Operability

The system left behind should not require Gradion to operate. Runbooks, architecture documentation, decision logs, and hands-on training ensure the internal team can maintain, extend, and debug the platform independently.

Data Governance & Residency During Migration

Migration is when data residency risks are highest. Data moving between legacy and modern systems - potentially across infrastructure providers - needs explicit governance at every stage.

For DACH clients and regulated environments, we design migration paths that maintain data residency compliance throughout the transition, not just at the destination. GDPR requirements, consent logic, and access controls are enforced in the migration layer. Where data sovereignty requires EU infrastructure, both the legacy and modern environments operate within compliant boundaries for the duration of the migration.

Proof of Production

Swiss Banking Technology Provider - 300+ Applications, FINMA-compliant Migration A multi-year cloud migration across more than 300 core banking applications, processing 500,000 daily transactions, under strict FINMA data sovereignty requirements. Gradion conducted the architecture review across all 300 applications, designed the compliant multi-cloud foundation on Azure and Google Cloud, and built the hybrid operational framework that allowed legacy systems to run alongside new cloud environments throughout the transition. Internal teams were trained and certified. The architecture passed a Big Four security and compliance audit without revisions. This is what phased migration looks like at regulated scale.

Vietnam’s largest coffee chain - Four Databases Consolidated, 12% Revenue Growth Vietnam's largest coffee chain operated 928 outlets across four separate databases that were never designed to work together. Finance, logistics, and sales data was reconciled manually. Gradion consolidated all four into a central data warehouse, connected key systems through custom APIs, and built a reporting layer that gave commercial and operations teams real-time visibility across every store. Revenue grew 12% within three months - because decisions that previously required a week of data preparation could be made the same day.

All figures are from live engagements. Additional references available under NDA.

AI Readiness

A modern data platform is the prerequisite for every AI initiative. If fragmented data infrastructure is the reason an AI pilot stalled - or if you want to ensure your platform can support AI workloads before you invest - the data assessment is the right starting point.

→ Explore AI Strategy & Readiness