Empower your business with data-driven decisions.
Unlock actionable insights and transform your data into business value with our tailored data engineering and analytics solutions.
Why data engineering and analytics matter
Most businesses don't lack data. It sits in the store, the ad accounts, analytics, email and the product itself, each describing customers and money a little differently.
Data engineering is the work of bringing it together reliably, agreeing what each number means, and keeping it flowing, so the people making decisions can trust what they see.
Data-driven solutions for scalable insights
From the pipelines that move your data to the dashboards and models that use it.
Data Engineering
Pipelines and storage built to be correct first and fast second.
Data pipeline development
Scheduled, independently retryable jobs from each source, so one failing API doesn't spoil a week of reporting.
Data warehousing
A warehouse modelled around the questions your business asks, with one definition per metric.
ETL/ELT processes
Raw platform exports turned into analytics-ready tables, with the rules for conflicting sources written down in code.
Device and sensor data
Real-time pipelines from connected devices to the cloud, with every reading traceable to its source.
Data Analytics
Reporting and analysis your team can use without waiting on an analyst.
Business intelligence (BI)
Dashboards your team uses directly for routine questions.
Customer and marketing metrics
CAC, LTV and campaign reporting across joined store, ad and email data.
Forecasting
Inventory reconciliation and demand forecasting from consolidated sales data.
Data strategy and consulting
Which sources matter, what each is the authority for, and what to build first.
What we build data systems with
- Python
- Apache Airflow
- PostgreSQL
- Apache Spark
- Snowflake
- Docker
- Google Cloud
- AWS
- Azure
- Metabase
- Tableau
- Power BI
What we usually find, and what we do about it
- 01The problem
Reporting done by hand in spreadsheets
What we doAutomated pipelines from each platform into one warehouse, so reports are current without anyone exporting and pasting.
- 02The problem
Sources that disagree
What we doTime zones, currency and what counts as a customer differ between platforms. We decide with you which source wins for each fact and write that into the transform layer, where it is visible.
- 03The problem
Pipelines that fail quietly
What we doPlatform APIs change and rate-limit. Each source runs as its own scheduled job with retries and clear dependencies, so a bad morning delays one source instead of corrupting everything.
From scattered sources to one source of truth
The metric definitions come before the pipelines, because a number nobody trusts is not worth automating.
- 01
Map the sources
Every platform that matters, and what each one is the authority for.
- 02
Agree the metrics
Definitions for CAC, LTV, revenue and the rest, agreed with your team before anything is built.
- 03
Build the pipelines
Scheduled, retryable jobs from each source into a warehouse modelled around your questions.
- 04
Report and validate
Dashboards checked against known figures with your team, number by number.
- 05
Run and extend
Monitoring, new sources and new reports as the business asks new questions.
Why trust our data expertise?
We have built data systems at both ends: pipelines from connected health devices to the cloud, and warehouses that bring commerce, marketing and email data into one place.
- Years of engineering
- 9+
- Engineers on the team
- 10+
- Platforms in one warehouse
- 4
How we work with data
- 01
Definitions before dashboards
Metrics are agreed with your team as they are built, not handed over as finished numbers to argue about.
- 02
Built to fail loudly
Retries, dependencies and alerts, so a broken source is noticed the same day.
- 03
From device to warehouse
The same team has built sensor pipelines for COR Health and a commerce warehouse for Avea Life.
- 04
Maintainable by your team
Containerised, documented and handed over properly, so the platform doesn't depend on us to keep running.
What Avea Life says about working with us
“They consistently went above and beyond their scope.”
Pascal RodeCo-founder, Avea Life
Read all our reviewsQuestions teams ask us
What industries do you specialize in?
Our focus is health and longevity products: connected devices, diagnostics and consumer health brands, where the data has to be reliable. The same data engineering serves clients in commerce and publishing.
How long does it take to implement a solution?
Timelines vary based on the project’s complexity. Most implementations take 4 to 12 weeks.
Do you provide ongoing support?
Yes, we offer post-deployment monitoring and support services.
Can you work with existing systems?
Yes. Most of our data work starts from platforms a business already runs, such as Shopify, Meta, Google Analytics and Klaviyo, and pulls from them through their APIs rather than replacing them.
How do you ensure data security?
We adhere to strict compliance standards and implement robust encryption and access controls.
Go deeper on data systems
COR Health
Real-time sensor pipelines from the device to the cloud, and the AI model trained on that data.
Read Insight · 6 minPractical AI for Business
Data engineering is the foundation for every AI system. Here is what production implementation actually involves.
Read Insight · 7 minBuilding a Connected Health Product
Where data pipelines sit in a connected health product, from the device to the person using it.
ReadReady to harness the power of your data?
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