# AI use cases for Laya (NandhaKishorM/laya) Repo: [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya) | Use cases: 3 | Page: https://dedupedai.com/use-cases/nandhakishorm-laya/ Laya is a multilingual, non-autoregressive decision engine. It answers typed questions (choice, score and noul yes/no probabilities) over any text, email, ticket or JSON document in a single forward pass, about 33 ms per question on a T4, with no text generation to parse. A Router picks between an English, a multilingual (100+ languages) and a typed-decisions checkpoint per request. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Route support tickets by department, urgency and churn risk: https://dedupedai.com/use-cases/nandhakishorm-laya/route-support-tickets-in-milliseconds/ - Triage customer messages in 100+ languages with one model: https://dedupedai.com/use-cases/nandhakishorm-laya/triage-messages-in-any-language/ - Fine-tune Laya on your own decisions for higher accuracy: https://dedupedai.com/use-cases/nandhakishorm-laya/fine-tune-laya-on-your-decisions/ ## How to route support tickets by department, urgency and churn risk with Laya Repo: [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya) | Difficulty: Beginner | Time: 30 minutes | Basis: From the README Ask Laya typed questions about each ticket and get a department, an urgency score and a churn probability. **Who it is for:** A support lead or developer who wants every incoming ticket triaged instantly without paying for LLM calls. **The problem:** Chat models are slow and costly for routing, and their free text answers have to be parsed and can be wrong in odd ways. **The outcome:** Structured answers per ticket, such as billing, blocking urgency and a churn probability, in milliseconds. ### Steps 1. Install with `python -m pip install laya` 2. Create a Router; it downloads a checkpoint on first use 3. Define questions as in the Quickstart: a choice for department, a score for urgency and a noul for churn risk 4. Call `router.predict(state, questions)` on each ticket text 5. Read the department choice and churn_risk probability and route the ticket ### Prompt to paste into your agent ```text Using NandhaKishorM/laya, write a script that reads tickets from , 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 Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/nandhakishorm-laya/route-support-tickets-in-milliseconds/ ## How to triage customer messages in 100+ languages with one model with Laya Repo: [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README Let the Router send non-English text to the multilingual checkpoint and get the same structured answers. **Who it is for:** A global support team that receives tickets in Hindi, Spanish and dozens of other languages. **The problem:** Translating every message before classifying it adds cost, delay and errors. **The outcome:** The same department and urgency decisions for any language, with the router reporting which checkpoint answered. ### Steps 1. Install Laya and build your questions as in the Quickstart 2. Pass non-English ticket text to `router.predict` without translating it 3. Check result routing model to see whether the multilingual checkpoint answered 4. For long documents pass model multilingual and max_len 8192, as the README warns 5. Try a quick check from the shell with `laya "My payment failed twice" --preset triage` ### Prompt to paste into your agent ```text Using NandhaKishorM/laya's Router, classify these customer messages in their original languages by department and urgency: . Print the routing model for each, and use model multilingual with max_len 8192 for any message longer than a page. ``` ### Caveat Beyond about 4,000 tokens of text the README reports results vary, so check long-document accuracy on your own data. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/nandhakishorm-laya/triage-messages-in-any-language/ ## How to fine-tune Laya on your own decisions for higher accuracy with Laya Repo: [NandhaKishorM/laya](https://github.com/NandhaKishorM/laya) | Difficulty: Advanced | Time: half a day | Basis: From the README Use the fine-tuning notebook or Apple Silicon script to train on decisions from your domain. **Who it is for:** An ML engineer whose zero-shot routing accuracy is not good enough for production. **The problem:** Shipped checkpoints work zero-shot, but domain specific decisions need domain data to be reliable. **The outcome:** A fine-tuned checkpoint trained with RLCD, calibrated and evaluated on your workflows. ### Steps 1. Collect labeled decisions from your own workflow 2. Open the Fine-tuning notebook (Kaggle 2xT4) or the Apple Silicon script from the README 3. Run the whole loop: build the dataset, train with RLCD, calibrate temperatures, evaluate and export 4. Compare accuracy against the base checkpoint on held-out decisions 5. Load the exported checkpoint in your Router ### Prompt to paste into your agent ```text Help me fine-tune NandhaKishorM/laya on my labeled decisions in using the README's Fine-tuning notebook flow. Build the dataset, train, calibrate and evaluate, then report accuracy against the base English checkpoint on a held-out split. ``` ### Caveat The README's accuracy jump (0.362 to 0.766) is on its own typed-decisions benchmark; your gain depends on your data. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/nandhakishorm-laya/fine-tune-laya-on-your-decisions/