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Data & AI Engineering
The foundation should be built to carry the first use case, not the fantasy.
Data platforms fail commercially when they are built for an imagined future rather than a funded outcome. We build the pipelines, warehouse and MLOps that your first valuable AI use case needs — then extend as each new use case pays for the next slice.
6 wks
to first sliceWorking pipeline serving a real decision
1 truth
per metricGoverned definitions replacing rival spreadsheets
−62%
pipeline costTypical saving after re-engineering legacy jobs
The eighteen-month platform that never served a decision.
We were asked to review a data programme eighteen months and several million dollars into delivery. The lake was elegant. The lineage diagrams were beautiful. Governance documentation ran to two hundred pages. Not one business decision was being made differently as a result, and the sponsor was about to lose their budget.
The failure was sequencing, not competence. The team had been asked to build a foundation for everything before anything was allowed to depend on it, so nothing ever did. Meanwhile the operational teams carried on running the business from exported spreadsheets.
We restarted from a single funded question — which customers to call today, and why — and built only the pipelines, models and definitions that question needed. It served a real decision in seven weeks. The rest of the platform then grew behind live use cases, each one paying for the slice beneath it. Foundations should be load-bearing, and load implies something standing on top.
We had bought a data platform. What we had needed was a decision that worked, and then a platform around it.
Everything under the model, built to be handed over.
We engineer for your team to own it. No proprietary layer, no dependency designed to keep us on the invoice.
Pipelines & integration
Batch, streaming and change-data-capture ingestion from your operational systems, SaaS platforms, files and devices, with schema-drift handling.
Warehouse & semantic layer
Dimensional models and a governed semantic layer so revenue, churn and margin mean one thing across every report and every model.
Feature store
Reusable, point-in-time-correct features shared between training and serving, eliminating the leakage that quietly ruins model performance in production.
MLOps & deployment
Versioned models, reproducible training, staged rollout, rollback and monitoring — the difference between a notebook and a production asset.
Data quality & observability
Contract tests, freshness and volume checks, anomaly detection and ownership, so problems are found by monitoring rather than by an executive.
Governance & cost management
Access control, lineage, retention, PII handling and per-workload cost visibility, because cloud data bills grow silently until someone owns them.
Why the engineering matters commercially.
Each of these is written into the engagement as a number with an owner, a baseline and a review date.
OUTCOME 01
AI stops stalling in pilot
The most common reason a promising model never reaches production is that nothing exists to serve it reliably. Solid engineering converts pilots into operating assets.
OUTCOME 02
Arguments about numbers end
A governed semantic layer removes the meeting where two departments dispute the same metric. Decisions get made in the time previously spent reconciling spreadsheets.
OUTCOME 03
Cloud spend becomes visible
Re-engineering inherited pipelines has cut client data platform costs by more than half while improving freshness — value that appears immediately in the operating budget.
A platform your engineers can run without us.
- Infrastructure as code in your cloud accounts, under your source control
- Tested, documented pipelines with named dataset ownership
- Semantic layer defining your core business metrics once
- Feature store and MLOps pipeline for training and serving
- Monitoring, alerting and cost attribution dashboards
- Handover: documentation, runbooks and paired working with your team
The engineering underneath.
Numbers from work already in production.
Measured against the baseline agreed with the client before the engagement started.
7 wks
To a live decisionRestarted programme, use-case-led rebuild
−62%
Pipeline running costAfter re-engineering inherited jobs
18 mo → 0
Unused platform workScope now gated on a funded use case
What data and platform leaders ask.
- 01. Do we need a warehouse before we can do AI?Not necessarily. We have delivered valuable AI directly against operational systems where the use case allowed it. What you need is reliable access to the right data, which is sometimes a warehouse and sometimes three well-built pipelines.
- 02. Will you lock us into your tooling?No. Everything runs on mainstream open or major-cloud technology, in your accounts, in your repositories. Our engagements are designed so that you can end them without dismantling anything.
- 03. Can you work with our existing data team?That is our preference. We pair with your engineers, adopt your conventions and aim to leave the team more capable than we found it — several clients now run everything we built with no external help.
- 04. How do you control cloud costs?Cost is a design constraint from day one: partitioning, incremental models, right-sized warehouses and per-workload attribution. Reviewing spend monthly is part of the engagement, not an afterthought.
Where to go next.
Name one decision you cannot make today. We will build the shortest path to it.
The engineering assessment reviews your estate, identifies the minimum foundation your first valuable use case needs and prices it against the value it unlocks.
