Data & AI engineering
From complex data to reliable AI
We design, build and operate data platforms, AI systems and production software for organisations where accuracy, traceability and compliance matter.
AI is only as reliable as the system around it
Real-world data arrives with gaps and errors, and it keeps changing.
We build the data controls and the software that keep AI reliable in production.
Exhibit 01
- Raw record
- Validated signal
- Uncertainty
- Decision
What arrives
Late, incomplete and contradictory records
What a decision needs
A range narrow enough to act on
- Raw sources
- Validated data
- Model
- Decision
Use the left and right arrow keys to read each stage of the chart.
One system, from source to decision
We build the three layers a reliable AI system needs and deliver them as production software. You can start with one layer and add the others when you need them.
Data Reliable by design
Platforms and pipelines that connect your sources, check every record and keep its history.
- Data platforms and pipelines, batch and streaming, on public, private or hybrid cloud
- Integration and harmonisation: HL7 FHIR, DICOM, OPC UA and common models such as OMOP CDM
- Data quality, lineage and access rules enforced by the platform
- Entity resolution: one record per patient, customer or asset
- Migration from legacy systems, reconciled record by record
AI Built to be checked
Models and AI systems that are tested before release and monitored in operation.
- Predictive models and forecasts with prediction intervals
- Assistants that cite their sources, and agents with human approval where risk requires it
- Document intelligence and computer vision with confidence scores and human review
- Evaluation, monitoring and retraining in production (MLOps and LLMOps)
- AI Act readiness, security testing and privacy engineering
Decisions Ready to act on
Analysis and tools that state their uncertainty and fit the decisions they support.
- Statistics, study design and real-world evidence
- Dashboards built on agreed metric definitions
- Optimisation and simulation to compare scenarios before committing
- Experiments that measure the real effect of a change
- Process mining from system logs
Production software
Back ends, APIs and integrations with ERP, electronic health record (EHR), MES and case-management systems, including the screens people use to review AI outputs.
Compliance built in
We treat the AI Act, GDPR, the European Health Data Space (EHDS), MDR/IVDR, the Data Act and NIS2 as technical requirements from the design stage.
We test our data methods on a system we run ourselves
We develop and operate a knowledge graph of European research and innovation, built only on public sources. It is where we build the data methods we apply for clients and research consortia.
Entity resolution: the same researcher or organisation matched across registries, designed to avoid false merges, with human review as the final authority.
Inference with evidence levels: every link is marked as verified, confirmed or inferred, and every figure shows its source.
Enrichment and harmonisation: public records normalised, linked through identifiers such as ORCID, ROR and PIC, and classified against controlled vocabularies.
Quality you can measure: automatic decisions are tested blind against data labelled by people.
Exhibit 02
Researcher
Organisation Verified
Project Confirmed
Principal investigator Inferred
- Verified Two independent registries agree.
- Confirmed One authoritative registry states it.
- Inferred Derived by us, and labelled as inferred.
We bring these methods to European and national research consortia as a technical partner, and plan how the results will reach production before the project ends.
Sectors where data and regulation are demanding
| Sector | The data problem | What we build | Standards |
|---|---|---|---|
Healthcare & life sciences |
Clinical, imaging and registry data that must be reused under consent and purpose rules. |
|
|
Public sector |
Decisions that must remain explainable to citizens. |
|
|
Financial services & insurance |
Models that supervisors must be able to explain. |
|
|
Industry & manufacturing |
Sensor, MES and ERP events that must be reconciled before any model can work. |
|
|
Energy & utilities |
Forecast errors that cost money in balancing and operations. |
|
|
Logistics & retail |
Demand, stock and transport planned in separate systems. |
|
Each step ends with a result you approve
Diagnostic
A fixed-scope review of one problem, dataset or process.
Result Assessment and recommendation, including whether to build at all
Proof of value
A short build on your real data, judged against criteria agreed before we start.
Result Go / no-go decision
Build & transfer
The system in production, with tests, documentation and training.
Result A system your team owns
Run & evolve
Operation and improvement under an agreed service level.
Result Monthly service report
Tell us what you need to solve
A few lines are enough. If it is outside what we do, we will tell you and suggest who can help.
Useful to include
- The decision you want to improve
- The systems and data involved
- Constraints: regulation, deadlines, budget