How the data moves
Your systems stay as they are. We add the steps that copy the data out, check it, and turn it into reports.
- You have
Your systems
Product database, business apps, files and event streams.
- Step 1
Pipelines
Scheduled and live jobs that copy the data in, with an alert if it stops.
- Step 2
Warehouse
All the data in one place, checked, modelled and documented.
- Step 3
Reports
Dashboards in the tool your team uses, or analytics inside your product.
Data engineering services
We build the pipelines, the warehouse and the reports, and hand them over with documentation your team can run.
Getting data in
- Batch pipelinesScheduled jobs from databases, files and APIs, safe to re-run.
- Live dataDatabase changes and events copied in as they happen.
- Scheduling and alertsJobs run in the right order, retry on failure, and alert when data stops.
- Taking over pipelinesExisting pipelines mapped and monitored, then fixed or rebuilt.
Using the data
- Warehouse designTables built around the questions the business asks, with each metric defined once.
- Data qualityChecks on missing values, ranges and row counts, with bad batches held back.
- DashboardsReports in the BI tool your team already uses.
- In-product analyticsReports for your customers, built into your product.
Where AI helps with data
When a question needs a prediction rather than a report, we add machine learning on top of the warehouse, using the same checked data.
Forecasting
Demand, sales or volume forecasts based on your own history.
Anomaly detection
Unusual numbers flagged before anyone acts on them.
Scoring
Records ranked or grouped, such as leads by likelihood to convert.
Tools we work with
We pick the tools that fit the cloud you already use and that your team can support.
Warehouses and storage
- BigQuery
- Snowflake
- Redshift
- PostgreSQL
- Amazon S3
- Google Cloud Storage
Clouds
- AWS
- Google Cloud
Questions we get on the first call
Have a question that is not here? Send it with the form below and an engineer will reply within one business day.
Which warehouse do you recommend?
It depends on where the rest of your systems run and how the data will be used: Redshift or Snowflake on AWS, BigQuery on Google Cloud, or PostgreSQL if your data fits. We do not recommend a platform your team cannot support.
Can you take over an existing pipeline?
Yes. We first map what runs, what it produces and what breaks, add monitoring, then fix or rebuild the parts that matter most.
How do you handle sensitive data?
Data is classified as it arrives, sensitive fields are masked, access is set by team and sensitivity, and use of the warehouse is logged.
Four ways to work with us
A defined project
A defined integration or product, priced and scheduled up front, delivered with the test suite and infrastructure code you keep.
Engineers on your team
Engineers who join your team, your code and your daily meetings for as long as the roadmap needs them.
Ongoing support
We keep the integrations you run working: monitoring, vendor updates, new connections and month-end support.
Taking over existing systems
A system or integration nobody wants to touch: we review it, make it stable, then build on it.
