Define the workflow
Select on-premise or private-cloud models around data sensitivity, infrastructure, operating cost and intended use.
Regulated Data & AI Engineering
Private AI & RAG for Regulated Environments
Private AI integrated into real workflows, with evaluation, access control and human review.
Engineer the system
Build access-controlled retrieval over approved documents with source references and explicit knowledge boundaries.
Review and operate
Evaluate retrieval and answer quality, failure cases and permission boundaries. Define human review and a controlled rollout.
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
Assess a private AI use case
Private AI integrated into real workflows, with evaluation, access control and human review.