Clinical standards and metadata
Clinical-data ingestion and harmonization, CDISC SDTM and ADaM mappings, and metadata-driven pipelines with explicit lineage.
Regulated Data & AI Engineering
Engineering for clinical and life-science data
Our primary specialization connects clinical-data standards, statistical programming and production software. We support Pharma, biotech, CROs and biometrics teams from ingestion and harmonization through controlled QC and reporting.
Statistical programming and QC
ADSL, ADAE and ADLB datasets, TLF generation, automated quality checks and Pinnacle 21 integration with controlled review of findings.
Interoperability and modernization
SAS, R and Python interoperability, legacy workflow modernization, reproducible reporting, APIs and analytical applications.
Agreed engineering deliverables
- Source-to-target mapping specifications and implemented pipeline
- QC outputs, review records and data-lineage documentation
- Reproducible reporting and validation-supporting evidence
Designed to support validation under the customer’s quality system. Validation responsibilities, acceptance criteria and human review are agreed explicitly; automation does not replace accountable quality review.
Services
Solutions
We combine scientific and statistical expertise with software engineering to modernize complex analytical workflows—from clinical-data processing to production applications.
Clinical Data Engineering & Automation
Our primary specialization connects clinical-data standards, statistical programming and production software. We support Pharma, biotech, CROs and biometrics teams from ingestion and harmonization through controlled QC and reporting.
Discuss a clinical-data workflowSDTM / ADaM Pipeline Automation
Metadata-driven clinical-data pipelines for biometrics, statistical programming and clinical data management teams.
Review your SDTM/ADaM pipelineLegacy Analytics Modernization
Reduce operational risk in critical R/SAS systems through reproducibility baselines, output comparison and controlled modernization.
Discuss your workflowPrivate AI & RAG for Regulated Environments
Private AI integrated into real workflows, with evaluation, access control and human review.
Assess a private AI use caseAI Production & Assurance
Move AI from prototype to controlled production with integration, evaluation and technical governance controls.
Discuss your workflowEngineering for clinical and life-science data
Our primary specialization connects clinical-data standards, statistical programming and production software. We support Pharma, biotech, CROs and biometrics teams from ingestion and harmonization through controlled QC and reporting.
Discuss a clinical-data workflowForward-Deployed Engineering & Strategic Consulting
Senior engineering collaboration remotely or on-site by agreement, and standalone strategic consulting for AI, data and regulated workflows.
Discuss your workflowEnterprise Assurance & Delivery Planning
Define the controls, responsibilities and evidence required for your engagement before access or implementation begins.
Discuss your workflowRegulated AI Readiness Assessment
Prepare AI initiatives for the EU AI Act, explainability, governance and defensible implementation.
Explore AI readinessCase Studies
Selected engineering projects
Anonymized project experience in Financial Services, Life Sciences and self-hosted AI automation, with concrete approaches, results and lessons learned.
Life Sciences & Biotechnology
AI-Assisted Audit Trail Reporting for an International Life Sciences Company
Human-reviewed AI-assisted development of structured audit trail reporting, with attention to maintainability, traceability and reproducibility.
Read the case studyTechnologies and standards chosen for the workflow
Statistical computing
R · Python · SAS · SQL
Clinical workflows
CDISC · SDTM · ADaM · Pinnacle 21 · TLF
Private AI
LLMs · RAG · on-premise · private cloud
Applications
Shiny · APIs · dashboards
Required inputs
Source schemas, representative approved data, metadata, applicable standards and current R/SAS interfaces.
Customer responsibilities
Provide authorized data access, clinical/statistical reviewers and quality-system requirements. The customer retains accountable quality review and formal validation responsibilities unless separately agreed.
Acceptance criteria
Agree mapping coverage, expected dataset structures, QC checks and review gates before implementation. Review exceptions, lineage and repeatability against the approved specifications.
Contact
Discuss a clinical-data workflow
Our primary specialization connects clinical-data standards, statistical programming and production software. We support Pharma, biotech, CROs and biometrics teams from ingestion and harmonization through controlled QC and reporting.