01 · Data foundations

Data foundations you can trust.

Warehouses, pipelines, dashboards — you have those. What's missing is the confidence to bet a real decision on the number in front of you.

Why it's hard

Moving data is easy.
Making it tell the truth isn't.

The problem is rarely a lack of data. It's what happens between disconnected systems and the decision at the other end.

Fragmented

ERP, CRM and spreadsheets. No shared definitions.

Unmapped

Critical relationships live in people's heads.

Untrusted

Same metric. Two dashboards. Two answers.

Stalled

Investment made. Confidence still missing.

How we approach it

From scattered systems to a model you can trust.

We make the journey visible: what comes in, how it gets reconciled and what becomes possible once the business has one dependable view of reality.

The data journey

Turn data that exists into data you can bet on.

SourcesERP · CRM · files · APIs

IngestBring it together

QualityReconcile + validate

Canonical modelOne business reality

Data productsReusable + governed

Analytics + AIDecisions that hold up

Governance · Security · Observability · Lineage

Business meaning comes first. The architecture follows - with quality, security and observability built across the whole path.

Selected work

See the before. See the after.

The transformation is easier to understand when the starting point and outcome sit side by side.

Data migrationGrocery retail

Teradata to Snowflake, at scale

Case study →

Before

180TB warehouse, growing 10TB/year

Top-five US grocery chain, $60B+ revenue

SLA pressure on critical batch workloads

After

Snowflake on Azure, elastic pay-as-you-go

Dedicated warehouse for critical batch

30%lower cost

Up to 40%faster queries

Data engineeringMarketing analytics

PCI-compliant data lake on AWS

Case study →

Before

Third-party transaction data at volume, no compliant platform

No MLOps foundation for the data science team

After

45TB PCI-compliant AWS lake, built in six months by one team

Ongoing embedded support for MLOps adoption

<$10K/mooperating cost

45TBscalable data lake

Proof, in numbers

$50k+/mo

Saved

Snowflake optimisation with no performance trade-off.

30%

Lower cost

180TB migrated to Snowflake with faster critical queries.

<$10k/mo

Operating cost

45TB PCI-compliant data lake, built from scratch on AWS.

Figures drawn from separate engagements — see the individual case studies for full context.

Under the hood

The capability map behind trusted data.

A compact view of the platforms, engineering disciplines and trust controls we use — without turning the page into a technology inventory.

Platforms

SnowflakeDatabricksAWSAzureGCP

Build

dbtSQLAirflowPythonTerraformCI/CD

Model Govern Deliver Observe

Trusted data
foundation

Trust + governance

ReconciliationData qualityObservabilityLineage

Deliver

Semantic layersPower BITableauLooker

Technology follows the problem. The constant is a foundation people can actually trust.

See all data case studies →

Once your data can be trusted, what can you build on it?

A trustworthy foundation is the precondition for AI that holds up in production — not demos that hallucinate.

AI, applied where it matters →

How we build

Every engineer builds with AI in the loop

And a human signs off every gate. That's what makes it faster without being reckless.

How we deliver →

Next step

Tell us where you've lost trust in your data.

We start by understanding your systems — how they connect, where the numbers diverge, what your metrics really mean — before proposing a thing.

Start a conversation →