Define the workflow
Inventory dependencies, analytical logic and operational risks. Establish representative baseline outputs and agree comparison tolerances with domain experts.
Regulated Data & AI Engineering
Legacy Analytics Modernization
Reduce operational risk in critical R/SAS systems through reproducibility baselines, output comparison and controlled modernization.
Engineer the system
Refactor or migrate R/SAS/Python workflows with regression tests, output comparisons and reusable R-modernization tooling. Preserve statistical intent and document changes.
Review and operate
Prepare reproducible environments, CI/CD, deployment and rollback guidance, documentation and validation-supporting evidence. Agree acceptance and handover to reduce key-person dependency.
Agreed engineering deliverables
- Dependency inventory and reproducibility baseline
- Modernized code, automated tests and output-comparison reports
- Deployment and rollback guidance with technical handover
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.
Case Studies
Selected engineering projects
Anonymized project experience in Financial Services, Life Sciences and self-hosted AI automation, with concrete approaches, results and lessons learned.
Banking & Financial Services
Enterprise Analytics & Data Engineering for a Major European Financial Institution
Project period: 2022–2024
Team-member experience combining R/Python analytics, data pipelines, database-backed applications and technical coordination in a regulated financial institution.
Read the case studyLife 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
Current code, dependencies, representative input/output pairs, execution environments and known operational constraints.
Customer responsibilities
Provide system access, subject-matter reviewers and owners for expected analytical behavior. Approve changes in scope and permissible output tolerances.
Acceptance criteria
Compare outputs with the agreed baseline and tolerances, investigate differences, and demonstrate repeatable execution plus deployment/rollback procedures.
Contact
Discuss your workflow
Reduce operational risk in critical R/SAS systems through reproducibility baselines, output comparison and controlled modernization.