Niblu's answer:

Niblu is menu-native. It reads real restaurant menus (photos/PDFs), then turns them into a clear decision: Safe, Unsafe, or Uncertain based on your personal allergy/intolerance profile, with the “why” behind each call.

Niblu's answer:

Most tools are barcode-first or database-first. Niblu is built for the moment you actually need help: when you’re staring at a menu at the table. It’s fast, personal, and improves over time as you save preferences and react to dishes.

Niblu's answer:

People who eat out and need to avoid specific ingredients due to allergies, intolerances, or restrictive diets (dairy-free, gluten-free, egg-free, nut-free, vegan, halal), especially travelers and busy diners.

Niblu's answer:

Next.js, TypeScript, React, PostgreSQL, Tailwind CSS, Three.js, plus AI vision + language models for menu understanding.

Niblu's answer:

Niblu started from the simple problem: eating out with restrictions is stressful, slow, and easy to mess up. The goal is to make restaurant dining safe and actually enjoyable again, with a tool that reads menus the way humans wish menus were written.

Niblu's answer:

• Individual diners managing allergies and intolerances (early adopters)
• Frequent travelers with restrictive diets
• Families and caregivers ordering for kids with inolerances
• Restaurant teams piloting allergen-friendly menu experiences