AI-ready data pipelines and platforms your team owns
AI is only as good as the data behind it. We connect your source systems, model the data and run it on a platform that scales, ready for analytics, predictive models and AI agents.
The engineering problems slowing everyone down
Pipelines break quietly
A failed job at 3am means stale dashboards by 9, and nobody knows until someone asks
Data lives in five tools
CRM, ERP, finance and spreadsheets never meet, so every report starts with an export
The platform costs too much
Cloud bills grow faster than the value, and nobody can say which workloads drive them
A platform that runs without heroics
Everything lives in your accounts and repositories, with tests and alerts so problems surface before users notice.
| Table | Owner | Freshness | Tests |
|---|---|---|---|
| orders_clean | Sales ops | Hourly | ✓ 14 |
| revenue_daily | Finance | 06:00 | ✓ 9 |
| customer_360 | Marketing | Daily | ✓ 21 |
- Source integrationConnectors and APIs for each system in scope, batch or streaming
- Modelled dataClean, documented tables built in dbt or your tool of choice
- OrchestrationScheduled, dependency-aware pipelines with retries
- Quality and monitoringAutomated tests, freshness checks and alerts
- Migration and modernisationMoves off legacy warehouses with reconciliation and rollback
- Runbook and handoverSo your team can run and extend it
Modernise without the big-bang risk
Legacy warehouses and hand-built pipelines get slower and more expensive as you grow. We plan and run the move to a modern platform in stages, checking the numbers match before each cutover.
Today
- Fragile, hand-built pipelines
- Rising cost with no clear owner
- Weeks to onboard a new source
- Migration stalled halfway
After
- Tested, documented pipelines on a modern platform
- Cost tracked per workload
- New sources added in days
- Staged cutover with matching numbers
Results you can check
We agree a baseline before work starts, so the result is measured, not claimed
From source audit to a platform you own
Source audit
Map systems, volumes and owners
Design
Architecture and data model for the first use case
Build
Pipelines and models in weekly increments
Test
Quality checks before anything goes live
Hand over
Monitoring, runbook and team training
We build on the stack you already run
- AWS
- Microsoft Azure
- Google Cloud
- Snowflake
- Databricks
- BigQuery
- dbt
- Airflow
- AWS Glue
- Kafka
- PySpark
- Python
See it in practice
From scattered HR data to workforce planning teams can act on
HR analytics for a Nestlé workforce division in North America, so planners can see available resources against production targets
What buyers ask before starting
Do we need a new data warehouse?
Not always. We start from what you run today.
Who owns the code?
You do, in your accounts, from day one.
Can you migrate us off a legacy warehouse?
Yes, with reconciliation checks before each cutover.