Build · Data estate modernisation
AI is only as good as the data behind it. Fragmented, slow or untrusted data is garbage in and garbage out, and in a regulated business it is a reputational and regulatory risk.
We modernise data estates from the source up, around the business outcomes that matter: quality, provenance and governance fixed first, then the architecture and platforms that deliver data at scale — so the AI products that follow have something solid to stand on.
THE PROBLEM
Data estates rarely fail loudly. They fail by being quietly routed around, until the AI programme asks a question they cannot answer. They succeed when they connect the right data, context and decisions to create business value.
Warehouses, lakes, spreadsheets, a legacy Teradata or Hadoop estate nobody wants to touch. Every question needs three systems and a person who knows which one is right this week.
Every team cleans the same data again in its own pipeline. The fixes never reach the source, so the problem never goes away.
Lineage, access control and audit exist in a policy. When the regulator asks where a number came from, the answer takes a week.
RAG pilots return confident nonsense because the data underneath is inconsistent. The data team gets the blame for a problem it has been flagging for years.
THE PROCESS
Data modernisation runs from three to nine months depending on the estate. Where the estate is large, the Map step runs Atlas across it.
Map
A data maturity and architecture assessment: current pipelines, storage structures, governance practices, quality at source, and who actually uses what. We trace the critical data flows end to end and quantify the cost of the workarounds.
You hold: A clear picture of the estate, the trust gaps, and the business cost of each one.
Design
Decoupled transactional and analytical layers, so analytics and AI can scale without touching live operations. Master data schemas and single entity identifiers give the business a golden thread. Governance, lineage and access control are designed into the architecture rather than written alongside it.
You hold: A target data architecture, a governance model and a sequenced migration plan.
Build
Pipelines and platforms built on Snowflake, Databricks or the hyperscaler-native stack, with automated data quality validation at source. Legacy warehouses are exited in waves with parallel running and automated reconciliation. Vector stores, embeddings and metadata tagging are added where AI retrieval needs them.
You hold: Fast, reliable, governed data delivered at scale, with the legacy estate retired safely.
Run
Monitoring, lineage and cost governance in place. Your data engineers own the platform and the quality pipelines, with the operating rhythm to keep trust high as the estate evolves.
You hold: A data estate your business trusts and your AI products can build on.
THE OUTCOME
What good looks like:
PARTNERS AND ECOSYSTEM
We are a Snowflake Services Select Partner and a Databricks Silver Partner with more than 50 Databricks-certified consultants, and we deliver on AWS, Azure and Google Cloud native data services at the same depth.
We move enterprises off legacy estates such as Teradata, Hadoop and SAS, and we build in the access controls, lineage and audit that regulated reporting demands. Platform choice follows the evidence from the Map step.
Services Select Partner.
One of the two platforms the Build step most often lands on, chosen on the evidence from the Map step rather than on a vendor relationship.
Silver Partner, 50+ certified consultants.
Enough certified people to staff a modernisation rather than advise on one, which is the difference between a recommendation and a delivery.
Migration and Modernisation Competency.
Where the data platform is hyperscaler-native rather than a third-party product, the same competency that funds the migration applies here too.
Alongside them we build with dbt and Terraform, and across AWS, Microsoft Azure and Google Cloud native data services.
BUILT IN-HOUSE, PROVEN ON CLIENT WORK
Reads the estate
An agentic discovery harness that profiles brownfield technology estates, mapping architecture, dependencies, resource usage and team ownership to expose risk, waste and modernisation opportunities.
For this work: On a large data estate Atlas maps the pipelines, schemas and consumers directly, so the Map step is built from what runs rather than from the last data catalogue someone updated.
LIVE IN BUSINESSES LIKE YOURS
PayPal
Petabytes of data across a global financial services business turned into real-time visibility, in a partnership now in its sixth year.
Read the case study
PA Media Group
A twenty-year-old racing data operation replatformed onto AWS in 14 weeks: new feed onboarding from 12 weeks to 1, race card build from 2.5 hours to under 15 minutes.
MONY Group
A cloud-native AWS product information management platform, with data quality, governance and identity control built in.
THE FIRST STEP
Two to four weeks gives you an honest picture of the estate, the trust gaps and what each one costs the business. It is the assessment the AI programme should have asked for first, and you keep it either way.
