JevGuard — Structured Uncertainty Primitives for LLM Apps

Let AI make structured choices or ask for clarification instead of guessing when information is incomplete.

Problem

LLM apps often generate answers under incomplete or ambiguous information, leading to wrong decisions; developers need a simple way to make AI perform structured choices or ask for clarification rather than free-form generation.

Solution

A lightweight SDK/API offering three primitives: Live Choice (structured selection among candidates), Noul (handling insufficient info with follow-up questions), and Score (rating candidate outputs), easily integrable into existing LLM workflows.

Users

AI app developers, especially those building customer support, scheduling, data extraction, or other high-reliability use cases.

MVP

Provide REST API or NPM package; implement basic logic for three primitives; support custom prompt templates; include simple examples and docs.

Revenue

Usage-based API pricing with free tier (1000 calls/month); or open-source core with enterprise plan for advanced monitoring and SLA.

Why now

LLM apps are maturing, demand for reliability and controllability rises; indie developers can quickly build and validate.