Who it is forA support lead or developer who wants every incoming ticket triaged instantly without paying for LLM calls.
The problemChat models are slow and costly for routing, and their free text answers have to be parsed and can be wrong in odd ways.
The outcomeStructured answers per ticket, such as billing, blocking urgency and a churn probability, in milliseconds.
What you need first
- Python 3.10 or newer
- pip or uv
- A GPU is optional; checkpoints download on first use
- Optional extras: laya[serve], laya[mcp], laya[langchain] and others
Steps
- Install with
python -m pip install laya - Create a Router; it downloads a checkpoint on first use
- Define questions as in the Quickstart: a choice for department, a score for urgency and a noul for churn risk
- Call
router.predict(state, questions)on each ticket text - Read the department choice and churn_risk probability and route the ticket
Prompt for your agent
Using NandhaKishorM/laya, write a script that reads tickets from <source>, asks a department choice (billing, technical, other), an urgency score and a churn_risk noul for each, and writes the answers to a CSV. Flag any ticket where churn_risk is above 0.7.
Caveat.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for NandhaKishorM/laya and "Route support tickets by department, urgency and churn risk"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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