We work with imperfect real-world data

Our models help users discover relevant offers, accurately compare products across different sellers, and account for personalized pricing, availability, delivery conditions, and search context.

ML solutions move quickly into production and directly influence search quality, conversion, user experience, and the overall economics of the product.

We build the algorithms responsible for the quality and intelligence of the product experience:

• ranking offers and products;
• semantic search and query understanding;
• matching identical or similar products across multiple sources;
• product deduplication and catalog normalization;
• filtering irrelevant, duplicate, or conflicting offers;
• search quality evaluation and offline/online metrics;
• personalization and user-context modeling;
• pricing, discount, availability, and personalized offer analysis;
• building training, validation, and model monitoring pipelines.

We work on problems at the intersection of search, recommendations, ranking, entity matching, price intelligence, and quality evaluation.
A big part of the challenge is dealing with imperfect real-world data:

• incomplete product titles;
• inconsistent descriptions and synonyms;
• brands, categories, and product attributes;
• pricing, availability, and user behavior signals.

Most of these problems cannot be solved with an off-the-shelf model alone.In practice, they require a combination of ML, product logic, feature engineering, high-quality labeling, evaluation metrics, and production-grade engineering.