Data architecture, pipelines, and governance
Warehouse and lakehouse design, streaming and batch ingestion, lineage, cataloguing, and ownership models that survive team change.
Capability 03 · Data & AI
Data platforms and AI systems designed to be trustworthy first: governed, observable, explainable — and only then fast.
The capability
Data programmes rarely fail on technology. They fail because nobody can say with confidence where a number came from, what changed overnight, or who is accountable when a model acts on bad input. We design platforms where lineage, quality, and ownership are properties of the system rather than conventions maintained by hand.
Where AI is involved, we treat model behaviour as a production concern: evaluated against labelled outcomes, observable in production, and bounded by explicit rules about what it may decide on its own.
Focus areas
Three areas, each delivered by practitioners who have operated them in production.
Warehouse and lakehouse design, streaming and batch ingestion, lineage, cataloguing, and ownership models that survive team change.
Semantic layers, governed metrics, and decision tooling built so a number means the same thing in every room it reaches.
Evaluation harnesses, human-in-the-loop boundaries, and model governance that keeps automated decisions auditable.
How we engage
The same discipline applies whether the engagement is a two-week decision sprint or a year-long programme.
Sources, consumers, ownership, and the trust level of every flow — starting with where the organisation already disagrees.
Canonical models, contracts between producers and consumers, and quality expectations defined before pipelines are written.
Pipelines shipped with lineage, freshness checks, schema-drift handling, and alerting wired in from the first commit.
Catalogues, ownership, and self-serve access patterns handed to your stewards so the platform keeps its promises.
All capabilities
Five capabilities, one standard of care — each led by practitioners who have delivered it in production at scale.
Independent counsel on technology direction — where to invest, what to modernise, and what to stop doing.
Cloud and application foundations engineered for reliability, security, and long-term maintainability.
Data platforms and AI systems designed to be trustworthy first — governed, observable, and explainable.
Programme-level delivery discipline for complex change — from re-platforming to operating-model reform.
Regulated and high-stakes sectors need context, not just capability. We bring both.
Working together
Scope is agreed against a named outcome before work begins, and the exit is agreed before the start.
Architecture and governance assessment with a prioritised remediation roadmap.
Design and delivery of pipelines, models, and controls against agreed acceptance criteria.
Evaluation of data fitness, risk exposure, and the case for a given AI use before you commit to it.
Sector experience
Domain context shapes the design from the first workshop — these are the sectors we know in production.
Related thinking
Case study · Financial services
How a payments provider moved from hourly batch processing to sub-10ms streaming fraud detection.
Read moreCase study · Healthcare
Multi-tenant architecture with row-level security that passed SOC 2 Type II with no non-conformities.
Read moreResearch · Agentic data
Where autonomous decision-making belongs in enterprise data systems — and where it does not.
Read moreHow we engage, who does the work, and what you can expect.
If you are weighing a consequential technology decision, we would be glad to discuss it — with no obligation and no sales process.
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