Health & life sciences

Real-world evidence and hospital operations, with patient data treated as such.

Privacy-preserving analytics over national registries, protocol-design automation for health-technology assessment, and AI-application development across hospital systems — delivered with EU AI Act and clinical-data discipline.

GDPR-nativeEU AI Act readinessClinical audit trail
Where we focus

Three focal points in this vertical.

01

Real-world data and evidence

Pipelines over inpatient records, dispensing data and civil registries, with protocol templates that compress follow-up cycles.

02

Hospital operations

Elective-recovery forecasting, capacity planning and AI-sovelluskehitys for the panels Europe's largest hospital systems put on frameworks.

03

Clinical document intelligence

Extraction and structured summarisation of clinical and regulatory documents, audited against human adjudication.

In practice

What this looks like on programme.

🇪🇪Estonia·Cross-sectorActive engagement

Data governance infrastructure for global consulting

McKinsey & Company

Problem

Managing the governance infrastructure of McKinsey's internal solutions portfolio at global scale, with strict security and compliance requirements.

Approach

Data pipeline orchestration across AWS services with infrastructure-as-code via Terraform, identity management through Keycloak, and continuous delivery across distributed teams.

Capability

Enterprise-grade DevOps, infrastructure governance, cloud-native data engineering.

AWSTerraformPythonEMRAthenaFirehose
🇳🇱Netherlands·EnterpriseDelivered

End-to-end ETL and ML platform for product intelligence

DSM

Problem

Product data scattered across APIs, S3, Athena, and web sources needed consolidation into a single platform serving in-house applications, customer-facing tools, BI dashboards, and ML predictions.

Approach

Built ETL system from scratch ingesting multi-source product data into PostgreSQL, exposed via Lambda APIs. Developed NLP models for customer support on SageMaker, deployed with MLflow. Managed a team of five engineers.

Capability

Full-stack data platform, NLP model deployment, team leadership.

AWSPythonAirflowGlueSageMakerTensorFlow
🇺🇸United States·Cloud infrastructureDelivered

ML-driven device identification and data infrastructure

Arista Networks

Problem

Cloud infrastructure provider needed automated scoring of websites and identification of incoming devices, plus reliable daily data pipelines and quality checks across GCP and AWS.

Approach

Developed and deployed ML scoring models, built data pipelines from databases to cloud storage, created Airflow DAGs for processing, backups, and data-quality monitoring. Code refactoring and dbt transformations across the stack.

Capability

ML model deployment at scale, cross-cloud data engineering, data quality automation.

GCPPythonSparkScalaPostgreSQLAirflow
Engage

Brief us on your programme.

We work with public-sector buyers in the UK, the Nordics, the Benelux and the Baltics. Tell us the problem — we'll tell you if we can help.