Semantic Search for Food Delivery Aggregators

A customer types "cold coffee" and gets zero results because your menu lists it as "Iced Coffee" or "Frappuccino." Someone searches "something spicy" and your keyword matcher doesn't know what to do with an adjective. A Hindi-speaking user types "बटर चिकन" in Devanagari and the same dish listed as "Butter Chicken" in English doesn't surface.

These failures are invisible in dashboards but they're measurable in revenue. Every null search result page is a customer who either scrolls manually, picks something they didn't want, or closes the app. Across millions of daily searches, even a small percentage of search abandonment compounds into significant lost GMV.

The root cause is simple: keyword matching treats dish names as raw strings and has zero understanding of food. It can't bridge "Murgh Makhani" and "Butter Chicken." It can't resolve transliterated Tamil or Telugu queries against English menus. It can't understand that "something sweet for dessert" should return Gulab Jamun and Tiramisu instead of items that happen to contain the word "sweet."

Latimal's /search endpoint replaces keyword matching with semantic understanding that's built specifically for food. It reads "Butter Chicken," "Murgh Makhani," "बटर चिकन," and "Murg Makkhni" as the same dish. Abstract queries like "something refreshing" or "light breakfast" return relevant results because the model understands food concepts at the semantic level.

Coverage spans 100+ languages with cross-script matching out of the box. Hindi-English transliteration, Tamil-to-English, Telugu-to-English, Arabic, Thai, Japanese, Korean: your search works for every customer, regardless of which script they type in.

Beyond search, the same API handles cuisine classification to auto-tag restaurants accurately, and cross-restaurant matching for price comparison across your catalog. You'll get search, discovery, and catalog intelligence from one integration.