We build healthcare data warehouses, data engineering at scale and AI integration in clinical workflows — with the domain depth that only comes from sustaining mission-critical clinical systems for three decades.
Clinical data has particular characteristics that don't appear in a conventional analytics project. Four sector realities that change the technical equation:
HL7 v2 continues to move messages in production alongside FHIR. DICOM for imaging. Proprietary encodings from systems installed fifteen years ago. Clinical reality is heterogeneous — a healthcare data project that doesn't account for this usually remains at pilot stage.
ICD-10-ES, SNOMED CT, LOINC, ATC. These are not tables you cross-reference in an afternoon. Each represents a distinct way of thinking about clinical practice. Working with them properly is as critical as sizing the infrastructure.
GDPR, ENS Nivel Alto, regional healthcare legislation. Every architectural decision — from data model to where the cluster is hosted — shapes the regulatory viability of the result.
An error in clinical data is not a wrong dashboard. It can be a misinformed clinical decision. Working here requires a validation discipline different from a conventional analytics project.
"Clinical context cannot be improvised. It is what turns a data pipeline into something healthcare can actually use."
We design and implement data warehouses built for healthcare reality: multi-source, multi-format, with lineage and traceability for every data point. The data model adapts to the clinical domain — patient, episode, test, report, clinician — so the system stays useful when questions change, without having to rebuild the foundation.
Ingestion pipelines that move real hospital volumes from heterogeneous sources — HL7, FHIR, DICOM, departmental systems, flat files — into the data warehouse. With quality control, validation and observability built into the flow itself. If a pipeline has a problem, we know about it before the customer does.
Operational reports, management dashboards, clinical indicators. The exploitation layer that converts the data warehouse into something a manager, a service head or an epidemiologist uses every week. This is also where AI-assisted automated BI generation lives — a capability already in use, which we will detail once fully deployed.
When a healthcare organisation wants to embed an AI model in its operations — diagnostic support, waiting list prioritisation, free-text report analysis — the main challenge is rarely the model itself. The real challenge is integration, prediction traceability, regulatory isolation, clinical interpretation of the result and the plan for when the model fails. The layer that turns an AI experiment into an operable clinical system.
"Turn an AI experiment into an operable clinical system. That is the real work."
Three decades working with real clinical data for Spain's public healthcare administration. Systems that today process data from thousands of professionals and millions of patients over time. Domain expertise is not something we picked up recently — it is the foundation of everything we do.
The technical capability we apply here transfers to other regulated verticals; when it comes to SaaS or HaaS product, that lives in Enterprise-grade SaaS platforms. When it is a service applied to healthcare, it lives here.
If you have a healthcare data warehouse to build, a migration of legacy reporting to modern BI, an integration of AI into clinical workflow, or simply the sense that your data could be delivering more — let's talk.