# Dense Retrieval

DevFeed: [Dense Retrieval](<https://devfeed.tech/articles/dense-retrieval-20440.md>)

Original publisher: [Read original article](<https://vinted.engineering//2025/11/18/dense-retrieval/>)

Author: Laurynas Jasiukėnas

Published: 2025-11-18T00:00:00Z

Content type: article

Language: en

Sources: [Vinted](<https://devfeed.tech/sources/vinted.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [rust-ai](<https://devfeed.tech/topics/rust-ai.md>)

Tags: [business](<https://devfeed.tech/tags/business.md>), [e-commerce](<https://devfeed.tech/tags/e-commerce.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [experimentation](<https://devfeed.tech/tags/experimentation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [images](<https://devfeed.tech/tags/images.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [model](<https://devfeed.tech/tags/model.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [two-tower-model](<https://devfeed.tech/tags/two-tower-model.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

Vinted Engineering describes integrating dense embedding-based retrieval into its multilingual e-commerce search system. The approach uses a two-tower model with query and item embeddings, and the article covers experimentation, model improvements, and engineering work that led to full enablement after approximately 50 A/B tests.

## Source excerpt

TL;DR: integrating embedding-based retrieval into the e-commerce search application is a significant undertaking.