01
Define the controlled workflow
Start with the user decision, source material, access rules and acceptable failure behavior before selecting model components.
Regulated AI Workflow Implementation
Production-oriented GenAI systems where data flow, access control, evaluation, auditability and operations matter as much as the model call.
Typical work
- RAG and document-processing workflow design
- Vertex AI, Gemini and Amazon Bedrock integration
- Agentic workflow prototyping and hardening
- Evaluation, tracing and operational visibility
- RBAC, data-flow and auditability planning
A good fit when
- Teams turning an AI prototype into a maintained product feature
- Document-heavy workflows that need retrieval and traceability
- Clinical or regulated products with strict access and data-handling constraints
How the engagement moves
02
Build the surrounding system
Implement retrieval, orchestration, permissions, structured telemetry and the interfaces around the model.
03
Make quality observable
Establish evaluation and operational signals so the workflow can be reviewed and improved after release.
What you can expect to receive
- Workflow and data-boundary design
- Model integration and application services
- Evaluation and observability approach
- Security, failure-mode and operating notes
Related case study
Regulated Clinical AI Platform
See how this type of work appears inside a real, anonymized production engagement.
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