Define the workflow
Define production architecture, system boundaries, data flows and access controls. Integrate the AI workflow with existing infrastructure and agree human oversight responsibilities.
Regulated Data & AI Engineering
AI Production & Assurance
Move AI from prototype to controlled production with integration, evaluation and technical governance controls.
Engineer the system
Implement evaluation criteria, failure and hallucination tests, prompt/model regression checks and traceable logs as required by the use case. Assess results against agreed acceptance thresholds.
Review and operate
Prepare deployment, documentation, operational procedures and handover. Scope model/vendor inventories, monitoring instrumentation, cost/performance checks and AI Act documentation support to the project. Ongoing operation is not included.
Agreed engineering deliverables
- Evaluation dataset, quality thresholds and evaluation report
- Regression tests, access model and logging configuration
- Deployment procedures, documented limitations and handover guidance
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.
Independent technology consulting
Automated Project Matching for an Independent Consultant
A self-hosted Python agent compares German and English freelance listings against multiple CVs and groups relevant opportunities into email digests. Time savings and match precision have not yet been measured.
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
Intended use, representative evaluation questions, approved knowledge sources, current prototype and infrastructure constraints.
Customer responsibilities
Provide domain reviewers, approved data access and owners for human oversight and operational decisions. Agree permitted model providers and data locations.
Acceptance criteria
Agree task-specific quality thresholds and failure cases. Demonstrate access boundaries, traceable outputs and repeatable regression checks before the agreed deployment.
Contact
Discuss your workflow
Move AI from prototype to controlled production with integration, evaluation and technical governance controls.