Routing use case
Route AI Requests By Data Sensitivity, Cost, And Risk
Teams start using multiple AI tools before deciding which data can go where, which model is worth the cost, and what needs to be logged.
AI routing layer compliance Switzerland
Swiss SME workflow
Workshop-ready
Human approval by design
Before AI
How it looks today
- Users paste data into whichever AI tool is open.
- Simple tasks use expensive models.
- Sensitive tasks have unclear approval rules.
- No one can explain which model handled which request.
What AI can do
The first useful pass
- Classify requests by data type and risk.
- Route simple tasks to cheaper models.
- Send sensitive work through approved providers or local routes.
- Log requests, model choice, cost, and approval steps.
Humans approve
What stays in human hands
- Data classification policy.
- Provider list and hosting assumptions.
- Requests touching client, HR, legal, financial, or regulated data.
Tools involved
The stack we usually meet
- OpenAI
- Anthropic
- Azure
- Swiss/EU hosting
- Vercel
- Railway
- Postgres
Workshop exercise
Route AI requests safely
Inventory 10 AI tasks, classify their data, choose routing rules, and prototype a simple cost and compliance decision table.
Expected outcome
A clear AI routing policy and implementation path that lowers cost without creating a data handling mess.
Bring this to a workshop →