Data platform & lakehouse
A well-layered architecture from raw to curated, built on open formats so you are never locked in.
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Data & Big Data
Foundations for everything downstream.
Almost every failed analytics or AI programme is, underneath, a data failure: numbers nobody trusts, pipelines that break silently, and no way to trace where anything came from. Get the data foundation right and everything downstream, dashboards, models, agents, becomes dramatically cheaper and more reliable.
We treat data as a product, with owners, contracts and quality guarantees, and we build the platform that makes that discipline sustainable rather than heroic.
What we do
The work, in detail.
Streaming & real-time
Event pipelines that move data in seconds, not overnight, where the business needs it fresh.
Batch pipelines
Robust, observable batch processing for the workloads where throughput matters more than latency.
Quality & data contracts
Automated checks and contracts that catch bad data at the boundary, not three dashboards later.
Governance & lineage
Cataloguing and end-to-end lineage so anyone can answer "where did this number come from?"
Analytics enablement
Warehouse modelling and self-serve tooling so analysts move quickly on data they can trust.
How we deliver
A disciplined path to production.
- 01
Audit the estate
Map sources, flows and the places data quietly goes wrong today.
- 02
Design the architecture
Choose layers, formats and processing patterns that fit your latency and scale needs.
- 03
Build the pipelines
Deliver batch and streaming flows incrementally, observable from the first day.
- 04
Establish quality & governance
Add contracts, tests, lineage and ownership so trust is built in, not bolted on.
- 05
Enable the teams
Model the warehouse and equip analysts and scientists to self-serve safely.
What you get
Outcomes, not artefacts.
- A single, trusted data foundation for analytics, ML and AI
- Real-time and batch pipelines that are observable and resilient
- Data quality guarantees enforced automatically
- Full lineage and a searchable catalogue
- Analysts and scientists unblocked and self-sufficient
Questions
Good questions, answered.
Rarely. We usually start with the highest-value use case, build the slice of foundation it genuinely needs, and design it to generalise, so the first project pays for itself and the next inherits the groundwork.
It depends entirely on how fresh the data must be for the decision it feeds. We help you choose per use case rather than dogmatically.
We favour open table formats and portable tooling, so your data and pipelines are not hostage to a single platform.
Related insights
All insights Further reading.
Why your data foundations decide whether AI succeeds
Most failed AI initiatives are not model failures, they are data failures. Here is how to build the foundation that makes everything downstream possible.
14 August 2026Read
Real-time or batch? Choosing the right data architecture
Streaming is not automatically better than batch. A practical way to decide how fresh your data really needs to be, and to avoid paying for latency you never use.
28 August 2026Read
Have something worth building well?
Whether you are starting from a blank page or rescuing something that has outgrown its foundations, let's talk about what good looks like.