Thalius combines advanced vector search with traditional keyword search, leveraging existing categories, tags, and product attributes.

This approach is the foundational layer, matching user queries with exact keywords in product data.

It utilizes advanced algorithms to prioritize keyword frequency, placement within content, and various metadata factors to ensure highly relevant literal matches.

This advanced layer interprets the meaning and intent behind queries using machine learning-generated embeddings (vector representations) that capture semantic relationships between words, products, and queries.

Thalius distinguishes itself by offering more transparent and controlled embeddings, meaning the transformation process from raw product data to vector representation is auditable, customizable, and not only reliant on third-party models.

Vector Search excels at dealing with synonyms, ambiguous phrasing, and even user spelling errors—retrieving relevant products even when queries and listings are not literal matches.

The embedding pipeline is optimized to require significantly less compute than large language models (LLMs), while remaining fully compatible with major e-commerce platforms.

This innovative layer goes beyond static results, enabling users to explore products through contextual, relational, or attribute-driven exploration paths.

Sliding and Toggle Search leverages structured and semantic relationships within the product catalog—such as category proximity, visual similarity, co-purchase patterns, and customer journey data—to create a fluid and intuitive discovery experience.

This approach is unique in the market: users can see and adjust how filters, attributes, or contextual relationships influence their results, for a much more intuitive and fun search experience!

This allows users to curate their personal taste profiles, enabling the discovery of not just visually or functionally similar items, but also complementary products that share the same underlying style or aesthetic, seamlessly extending personal taste to new product categories.

Thalius myTaste uses a unique embedding technique that builds a "taste profile" for each user by creating a multidimensional representation of their selected products and styles. Instead of focusing only on historical purchases or similar products, this approach generates a continuous, high-resolution vector that captures key visual, functional, and stylistic attributes across user selections.

By leveraging advanced, customizable embeddings, Thalius myTaste can match not just items with similar surface traits, but also identify complementary products that align with the same underlying aesthetic, which allows the user’s personal style to be extended seamlessly across categories.

This embedding method ensures that the system understands complex relationships between products based on user-curated tastes, empowering more accurate and holistic discovery experiences than conventional similarity or collaborative filtering models.