Source integration
Connection to ERP, ecommerce, media platforms, CRM and spreadsheets, respecting each API's limits and cost.
Service
ETL/ELT pipelines
Data flowing from source to analysis with monitoring and error handling. A pipeline that fails in silence is worse than none at all.
The dangerous pipeline is not the one that breaks. It is the one that breaks quietly and keeps feeding stale data into a dashboard somebody is using to decide. Every flow we build says when it failed and what it left behind.
Book a 30-min conversationWhat this service covers
Deliverables that work together or separately, depending on where your brand stands.
Orchestrated pipelines
Flows with defined dependency and order, so nothing runs on data that has not arrived yet.
Transformation and cleaning
Rules for deduplication, standardisation and validation applied where they belong, with the original preserved.
Monitoring and alerts
Every failure produces a notification with enough context to act, not a generic error in a log nobody reads.
Reprocessing
The ability to rerun a window when a source corrects data after the fact, without rebuilding everything.
Documentation and handover
Flow diagram, dependencies and recovery procedures written for whoever is on call.
How we work
A clear method. A partner who stays when execution starts.
A silent failure is a bug
A pipeline that fails without alerting is treated as broken, even when the job returns success.
The original is preserved
Transformation never overwrites the raw data. If a rule turns out wrong, reprocessing is possible.
Cost enters the design
Ingest frequency and volume are decided with the API bill in mind, not only the technical ideal.
Idempotent by design
Running the same flow twice produces the same result. That is what makes recovery safe.
Documentation for on call
Whoever gets the 2am alert has to find the recovery procedure written, not reverse engineer it.
Frequently asked questions
How often can data be updated?
From near real time to daily, depending on the source and the decision it feeds. Not every metric needs to be fresh by the minute, and frequency costs money.
What if a source changes its API?
Monitoring catches the break and we adjust the connector. That is part of the maintenance cycle, not an extra project.
Do you work with which tools?
We use what fits the scenario, from managed services to Python orchestration. The choice follows your team's ability to maintain it.
What about data that arrives dirty?
Cleaning rules are explicit and on record. What cannot be resolved automatically goes into an exception report rather than being silently dropped.
Can my team maintain it afterwards?
Yes, and that is the intent. Documentation and handover are part of the delivery.
Do I need a warehouse for this?
Not always. Where to land the data is a decision we make in the diagnosis, based on volume and use.
Want to understand how this applies to your brand?
Tell us about your operation. We will say honestly what can be delivered and how long it takes.
Get in touch