Confidentiality, access and deployment
Discuss confidentiality terms, GDPR roles, approved data locations, access permissions and hosting constraints. Agree whether the solution belongs on-premise or in an approved private cloud.
Regulated Data & AI Engineering
Enterprise Assurance & Delivery Planning
Define the controls, responsibilities and evidence required for your engagement before access or implementation begins.
Quality and implementation evidence
Agree the applicable development lifecycle, code-review and test requirements, version control, documentation, AI evaluation and customer validation responsibilities. Required evidence and acceptance criteria belong in the project scope.
Operations and continuity
Clarify ownership of backups, recovery, maintenance, incident handling and handover. Identify any proposed subcontractors and approval requirements. These topics describe procurement planning; project-specific commitments are documented in the agreement.
Agreed engineering deliverables
- Workflow and architecture specification
- Documented implementation and test evidence
- Review, deployment and handover plan
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.
Technologies 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
Contact
Discuss your workflow
Define the controls, responsibilities and evidence required for your engagement before access or implementation begins.