Turn your scattered data into a strategy
We build the pipelines to gather the data you actually need: scraping the web, calling external APIs, tapping legacy databases and files. Then we clean it and unify it into one platform your team can actually build on.
- Every data source unified into one reliable platform
- Real-time and batch pipelines at your actual scale
- Your analysts don't have to wait on a developer
- Problems surface before they reach a report
Your data exists. It just isn't working for you.
Your data isn't in one place
Some data sits in a spreadsheet, some behind an API, some in a legacy system. And you're not even sure this is all the data there is.
Every question is a developer ticket
Your analysts can see what they're given but anything beyond the standard report means waiting in a queue.
Built on untrustworthy data
No quality checks, no monitoring on the pipelines themselves. All issues surface only once someone's already made a decision on bad data.
A reliable data platform is the foundation every other business intelligence investment depends on.
These problems usually share a root cause: data spread across systems that were never built to talk to each other. We fix that by owning the whole pipeline. Here's how we do it differently:
One platform. One source of truth.
We build data infrastructure that's engineered for reliability, not just functionality. That means proper data modelling, automated testing, observability from day one, and documentation that doesn't rot.
One reliable platform
All your data sources unified into a single, queryable system your whole organisation can trust. No more conflicting numbers between teams.
Pipelines that actually work
From web scraping and external API enrichment to legacy databases, flat files and live event streams, tested, documented and version-controlled like software.
Data quality built in
Automated checks, validation rules and alerts that catch quality issues before they reach your dashboards or reports.
Real-time or scheduled
Streaming pipelines for live decisions, batch pipelines for heavy workloads. We build the right architecture for your use case.
Self-service for your team
We configure your BI tools and train your analysts so they can answer questions independently, without waiting on engineering.
Full observability
Pipeline health, data freshness and query performance monitored continuously. You know if something breaks before your users do.
A few ways teams use this.
Reconcile sales, finance and ops numbers into one figure everyone trusts
Merge several SaaS tools and a legacy database into one queryable source
A daily competitor-pricing scrape landed in your warehouse
A real-time event stream feeding a live operations dashboard
How we work.
A clear process from first conversation to final delivery.
Discovery
We start with a call and follow-up meetings to fully understand your situation, goals and requirements.
Concept
Based on what we've learned, we develop an initial direction and present it for your review.
Feedback
You share your thoughts, we refine. If needed, we go back to concepting until we get it right.
Approval
We align on the final result together before moving into production or finalisation.
Delivery
Handover, launch or go-live. Depending on the project, we make sure everything lands properly.
Unreliable data doesn't just slow decisions, it quietly corrupts them. We build pipelines you can trust so your team stops second-guessing every number.
— Freddy Leemans, Data & AI Engineer at Setten
Let's talk about your project.
Tell us where you are and where you want to go. We'll come back to you within one business day with honest feedback and a clear next step.
Data Engineering questions, answered.
We've built platforms on AWS (Redshift, Glue, Kinesis), GCP (BigQuery, Dataflow, Pub/Sub), and Azure (Synapse, Data Factory, Event Hubs). We work across AWS, GCP and Azure and will recommend the platform that fits your existing infrastructure and team skills.
Yes, it's the norm rather than the exception. Most clients have a mix of cloud SaaS tools and on-premises systems. We design ingestion layers that pull from both without requiring you to migrate everything to the cloud first.
Data governance is designed into the architecture from the start, not retrofitted. We implement row-level security, column masking, data cataloguing, and audit logging as part of the standard platform build.
Yes. We often inherit platforms built by previous teams or vendors. We start with a full audit, document what we find (including technical debt), and agree on a remediation plan before making changes.
Ready to grow your business?
Book a 30-minute call. No pitch deck, no pressure, just an honest conversation about your challenge and how we can help.