Lakehouse and warehouse design
A modern platform on Databricks, Snowflake or BigQuery, sized for your volumes and your budget.
Data Platforms & Engineering
Data platforms that make every model downstream useful.
We build modern lakehouses, clean pipelines and governed semantic layers. It is the unglamorous foundation that decides whether analytics are trusted and whether AI works at all, and we build it inside the cloud you already use.
The problem
Why this work usually stalls.
01
Data scattered across tools
Sales, finance, product and operations each hold part of the truth in different systems and spreadsheets.
02
Numbers nobody trusts
Two dashboards show two different revenue figures, so meetings start with an argument about the data.
03
Fragile pipelines
Hand-written scripts break quietly, and someone finds out when a report is wrong.
04
Not ready for AI
Models need clean, connected, well-documented data. Without it, every AI project starts with months of wrangling.
What we deliver
Data Platforms & Engineering, built for production.
Pick one piece or the whole programme. Each is scoped to a business metric and shipped, not left as a proof of concept.
ELT pipelines
Reliable, tested pipelines from your apps, databases and SaaS tools, with scheduling and alerting.
Data quality and observability
Tests and monitoring on the data itself, so problems are caught before they reach a dashboard or a model.
Semantic layer and metrics
One governed definition of every business metric, used by every dashboard and every model.
Streaming data
Real-time pipelines for fraud signals, operations and product events where batch is too slow.
Migration and modernisation
Moving from legacy warehouses and scripts to a modern platform without breaking the reports people rely on.
How we deliver
From first call to production.
Every phase ends with something you can use, not just something you can read.
Always included
- A senior squad of two to four people, led by an engineer from companies like Amazon, Google or Microsoft
- Weekly demos and one business metric agreed upfront
- Founder review every week and sign-off before production
- All code, models and documentation owned by you
Typical tools
01
Weeks 1 to 2
Data audit
We learn where your data lives and which numbers matter most.
- Inventory of sources, pipelines and reports
- Quality and freshness checks on key tables
- Agree the first metrics to fix
02
Weeks 2 to 3
Target architecture
A platform design that fits your cloud, your team and your budget.
- Platform and tooling choices with trade-offs
- Data model and naming standards
- Access, security and cost controls
03
Weeks 3 to 8
Build the core
Pipelines, models and the first trusted metrics, shipped weekly.
- Tested pipelines for priority sources
- Semantic layer for core metrics
- Monitoring and alerting switched on
04
Ongoing
Expand and hand over
More sources and use cases, then a clean hand-off or managed operation.
- Documentation and runbooks your team owns
- Training for analysts and engineers
- Optional managed data platform support
Where we apply it
Industries we know well.
Further reading
From the blog.
AI Readiness Assessment: A Practical Checklist for Leaders
A scored AI readiness assessment checklist covering strategy, data, technology, people and governance, with a simple rubric and guidance on what to fix first.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
FAQ
Questions we hear a lot.
Should we choose Databricks, Snowflake or BigQuery?
It depends on your cloud, your team's skills, your workloads and your budget. All three are strong. We recommend based on what you already run and what you plan to do with AI and analytics, not on vendor preference.
Do we need a data platform before starting AI?
Not always. Many AI use cases can start with a narrow, well-defined data set. But if several AI and analytics projects are planned, a shared platform saves time and money on every one after the first.
Can you work with our existing pipelines?
Yes. We usually keep what works, fix what is fragile and migrate in stages, so reports keep running throughout.
Who runs the platform after you build it?
Your team, with documentation and training from us, or we can run it for you as part of our managed services.
Other services
Everything between ambition and production.
GenAI & AI Agents
Copilots, retrieval systems and agents wired into your real tools and data.
AI Strategy & Roadmaps
Find where AI moves your numbers and sequence a roadmap people believe in.
Analytics & Decision Intelligence
Forecasting, pricing, churn and demand models, plus dashboards people open.
MLOps & AI Engineering
CI/CD for models, observability and cost controls that keep AI reliable.
Digital Transformation & AI Governance
Process redesign, change and responsible AI aligned with the DPDP Act and EU AI Act.
Managed Services
Cloud, DevOps, QA and release management, run 24x7 against clear SLAs.
Talk to a founder about Data Platforms.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.