A pocket food translator for the 1.4 billion people who cross a border each year. Snap any dish, get the ingredients, allergens, and taste profile in under a second — then follow what the community is eating around you.
Why now
The on-the-ground food experience hasn't been solved. Google Translate guesses at menu text. Yelp ranks places by review count, not by what's actually on the plate. Travelers with dietary needs — peanut allergies, halal, gluten, religious restrictions — are still pulling out flashcards in 2026.
Three things changed this year: multimodal vision models got good enough to identify regional dishes from a photo, the cost per scan dropped below $0.01, and a generation of travelers learned to trust AI for everyday decisions. Nom Nom Go is built on all three.
Traction
The thesis
Allergens first
We win the user with the most expensive problem: a peanut allergy in Hanoi. From there we expand to discovery.
Scans build the map
Every analysis becomes a community pin. The data moat compounds with each user, in each city.
Travel-first, then home
Travel is the moment of highest pain and intent. We earn trust abroad, then become the default at home.
"It was the first time on this trip I didn't have to interrogate the waiter about peanuts."
The round
Raising to scale the map.
This round funds 18 months of engineering, city-by-city expansion across SEA and Latin America, and a small growth team. We have a clear path to series A on consumer revenue + B2B (insurance, allergy programs).
Team
Founded by a small team of operators and engineers with backgrounds in consumer apps, multimodal ML, and travel. Headquartered in Bangkok, with a foot in San Francisco. Bios and references shared on request.