Conversational AI that restores autonomy at the table

Overview

Autonomy is the product outcome, not just access

Most accessible menu approaches treat the problem as text extraction. That still leaves diners without structure, context, or a way to ask follow-ups under real restaurant pressure. Menu-B helps people explore and decide through a calm multimodal conversation: clarity and trust over novelty.

Origin

“If it's a problem for you, it's probably a problem for me too”

A lunch with my friend Eshaan Sood (@thejumpymonkey) rewrote the brief. I arrived with a banking idea for blind users; he explained banking is already highly accessible. The shared friction was the menu itself: vague dishes, no descriptions, no way to ask. That is how I was inspired to build Menu-B, co-designed with lived experience rather than assumed alone.

Problem

Dining is universal.Accessible menus are not

Menus are still designed as visual artifacts first, which quietly strips autonomy from blind and low-vision diners when confidence matters most.

Solution

Menu-B: conversation as the access layer

Inspired by how bees move from flower to flower, and by the idea of a "Menu B," Menu-B transforms the traditional menu into an interactive experience. Instead of scanning dense text, diners explore structured dish information through a calm, multimodal conversation built for real restaurant settings.

Ecosystem

Designing beyond the chat interface

Independence at the table is an ecosystem: menus and scanning, restaurants and data, AI and accessibility tooling, trust, privacy, and social pressure. Mapping those forces early kept decisions grounded in the full dining context, not only the conversation UI.

Impact

Designed for blind users.Built as universal design

Primary users are blind and low-vision diners, but the same friction shows up more broadly. Solving the hardest case strengthens the experience for everyone at the table.

Interaction

Multimodal by default.Switch modes without losing the thread

Voice and text, interchangeable in one conversation thread, so diners choose what fits the moment without restarting context.

Bot personality

Personality as an accessibility decision

Personality was treated as cognitive load management: calm, steady, slightly formal; mid-to-low personification so warmth never distracts from food, allergens, or cost decisions.

Conversation design

Flexible entry points, consistent structure

Architecture supports full menu, section, and custom queries while keeping responses consistent, contextual, and navigable. Trust earned turn by turn.

Scaling

Trust scales only when data does

Reflections

What I carry forward